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Introduction to the Special Issue on Delay-Sensitive Video Computing in the Cloud

Delay-Sensitive Video Computing in the Cloud: A Survey

While cloud servers provide a tremendous amount of resources for networked video applications, most successful stories of cloud-assisted video applications are presentational video services, such as YouTube and NetFlix. This article surveys the recent advances on delay-sensitive video computations in the cloud, which are crucial to cloud-assisted... (more)

Cost-Efficient Server Provisioning for Cloud Gaming

Cloud gaming has gained significant popularity recently due to many important benefits such as removal of device constraints, instant-on, and... (more)

Game Categorization for Deriving QoE-Driven Video Encoding Configuration Strategies for Cloud Gaming

Cloud gaming has been recognized as a promising shift in the online game industry, with the aim of... (more)

Game Input with Delay—Moving Target Selection with a Game Controller Thumbstick

Hosting interactive video-based services, such as computer games, in the Cloud poses particular challenges given user sensitivity to delay. A better... (more)

Novel Hybrid-Cast Approach to Reduce Bandwidth and Latency for Cloud-Based Virtual Space

In this article, we explore the possibility of enabling cloud-based virtual space applications for better computational scalability and easy access... (more)

Cloud Baking: Collaborative Scene Illumination for Dynamic Web3D Scenes

We propose Cloud Baking, a collaborative rendering architecture for dynamic Web3D scenes. In our architecture, the cloud renderer renders the scene with the global illumination (GI) information in a GI map; the web-based client renderer renders the scene with ambient lighting only and blends it with the GI map received from the cloud for the final... (more)

ASAP: Adaptive Stall-Aware Pacing for Improved DASH Video Experience in Cellular Networks

The dramatic growth of video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent characteristics of wireless networks and varying channel conditions as well as variability in... (more)

On the Effectiveness of Offset Projections for 360-Degree Video Streaming

A new generation of video streaming technology, 360-degree video, promises greater immersiveness than standard video streams. This level of... (more)

NEWS

[July 2018]

Special issue call: "Face Analysis for Applications". Call for papers   Submission deadline Oct. 14th, 2018

 

[June 2018]

Call for Nominations for TOMM Nicolas D. Georganas Best Paper Award 2018

The Editor-in-Chief of ACM TOMM invites nominations for the ACM TOMM Nicolas D. Georganas Best Paper. Deadline for nominations of papers published in ACM TOMM from January 2017 to December 2017, is July 10th, 2018. See the call for nomination cfn

[May 2018]

Special issue call: "Affective Computing for Large-Scale Heterogeneous Multimedia Data". Cfp Submission deadline Dec. 15th, 2018

 

[April 2018]

Special Issue call: "Big Data, Machine Learning and AI Technologies for Art and Design". CfpSubmission deadline June 15th, 2018 Extended to August 31st 2018

 

[February 2018]

Special Issue on "Cross-Media Analysis for Visual Questions" Cfp Submission deadline June 30th 2018 Extended to July 31th, 2018

[October 2017]

2018/2019 SPECIAL ISSUE CALL 

We invite highly qualified scientists to submit proposals for 2018-19 ACM TOMM Special Issues. Each Special Issue is in the responsibility of the Guest Editors. Proposals are accepted until December 31st, 2017. They should be prepared according to the instructions outlined below, and sent by e-mail to the Information Director Stefano Berretti ([email protected]) and the Editor in Chief of ACM TOMM Alberto del Bimbo ([email protected]). More information about the proposals submission can be found in the CfP.

 

[June 2017]

The Impact Factor for the year 2016 is now available. ACM TOMM increased its IF from 0.982 to 2.250 being now the second ranked journal in the area of Multimedia. Thank you to all the EB members, authors, reviewers and readers for this excellent results.

Special Issue on " Multi-modal Understanding of Social, Affective and Subjective Attributes of Data". Cfp . Submission deadline Oct. 1st 2017

Special Issue on "Deep Learning for Intelligent Multimedia Analytics". Cfp. Submission deadline Oct. 15 2017

[April 2017]

Special Issue on  "QoE Management for Multimedia Services". Cfp Submission deadline May 15, 2017 Extended to June 15, 2017

[April 2017]

Call for Nominations for TOMM Nicolas D. Georganas Best Paper Award 2017

The Editor-in-Chief of ACM TOMM invites nominations for the ACM TOMM Nicolas D. Georganas Best Paper. Deadline for nominations of papers published in ACM TOMM from January 2016 to December 2016, is June 15th, 2017. See the call for nomination  cfn

[February 2017]

Upcoming special issues:

- "Delay-Sensitive Video Computing in the Cloud". Cfp   Submission deadline  Aug. 20, 2017

- "QoE Management for Multimedia Services". Cfp Submission deadline May 15, 2017

- "Representation, Analysis and Recognition of 3D Humans" Call for papers 

[January 2017]

ACM TOMM AE guidelines have been added

[December 2016]

ACM TOMM Special Issue on "Delay-Sensitive Video Computing in the Cloud". Cfp Submission deadline Nov. 30, 2016 Extended to Dec. 30, 2016

[November 2016]

- ACM TOMM Special Issue on "Deep Learning for Mobile Multimedia". Cfp  Submission deadline Oct15, 2016 Extended to Nov. 25, 2016

- Special Section on "Multimedia Computing and Applications of Socio-Affective Behaviors in the Wild"Cfp Submission deadline Oct. 31, 2016 Extended to Nov. 25, 2016

- Special Section on "Multimedia Understanding via Multimodal Analytics". Cfp Submission deadline Oct. 31, 2016 Extended to Nov. 25, 2016

 

[September 2016]

 

The 2016 ACM Transactions on Multimedia Computing, Communications and Applications (TOMM) Nicolas D. Georganas Best Paper Award is provided to the paper “Cross-Platform Emerging Topic Detection and Elaboration from Multimedia Streams” (TOMM vol. 11, Issue 4) by Bing-Kun Bao, Changsheng Xu, Weiqing Min and Mohammod Shamim Hossain. 

Dr. Cheng-Hsin Hsu has been nominated the ACM TOMM Associate Editor of the Year for 2016! Congratulations to Cheng-Hsin!

[August 2016]

Call for Nominations for TOMM Nicolas D. Georganas Best Paper Award

The Editor-in-Chief of ACM TOMM invites nominations for the ACM TOMM Nicolas D. Georganas Best Paper. Deadline for nominations of papers published in ACM TOMM from January 2015 to December 2015, is September 10th, 2016. See the cfn

[June 2016]

Forthcoming Special Issues in 2017

We received 11 competitive proposals this year, and we had limited slots available, so it was a very tough decision. At the end, the following four SI proposals have been selected and scheduled as follows:

- "Deep Learning for Mobile Multimedia". Cfp  Submission deadline Oct. 15, 2016 Extended to Oct. 31, 2016

- "Representation, Analysis and Recognition of 3D Human". Cfp   Submission deadline Jan. 15, 2017 Extended to Feb. 15, 2017 

Two Special Section have been also accepted and scheduled for publication in 2017:

- "Multimedia Computing and Applications of Socio-Affective Behaviors in the Wild". Cfp Submission deadline Oct. 31, 2016

- "Multimedia Understanding via Multimodal Analytics". Cfp Submission deadline Oct. 31, 2016

[June 2016]

Forthcoming Special Issues in 2016

"Trust Management for Multimedia Big Data" - Publication date August 2016

"Multimedia Big Data: Networking" - Publication date November 2016

[February 2016]

Advisory Board

We have created the ACM TOMM Advisory Board to support the Editor in Chief in the definition and implementation of strategies with no editorial duties. The following colleagues have been appointed as members of the ACM TOMM Advisory Board: Prof. Wen Gao,  Peking University, Prof. Arnold Smeulders, University of Amsterdam, Prof. Nicu Sebe, University of Trento. 

[January 2016]

New Assistant Information Director

Starting on January 1st 2016, Marco Bertini will be in charge of Assistant Information Director of ACM TOMM. 

[January 2016]

New Information Director         Starting on January 1st 2016, Stefano Berretti will be in charge of Information Director of ACM TOMM.  

[January 2016]

New Editor-in-Chief

After the end of the second term of Ralf Steinmetz, Alberto Del Bimbo from the University of Florence will be the next TOMM Editor-in-Chief starting on January 1st 2016. 

ACM TOMM Nicolas D. Georgans Best Paper Award 2015

The award goes to the article "A Quality of Experience Model for Haptic Virtual Environments” (TOMM vol.10, Issue 3) by Abdelwahab Hamam, Abdulmotaleb El Saddik and Jihad Alja'am. Congratulations!

ACM TOMM Associate Editor of the Year 2015 Award

The award goes Pradeep Atrey from State University of New York, USA for his excellent work for the journal. Congratulations!

CfP: Special Issue "Multimedia Big Data: Networking"

Please consider submitting to the second special issue in next years special issue series. Call for Papers

 

CfP: Special Issue "Trust Management for Multimedia Big Data"

Next year, TOMM will feature a special issue series on "Multimedia Big Data". First topic will be "Trust Management". Extended Deadline: October 15th! Call for Papers

 

Call for Nominations TOMM Editor-in-Chief

After two terms of the current EiC Ralf Steinmetz, the search committee started the search for a new Editor-in-Chief. Call for Nominations

 

New ACM submission templates

The new ACM submission templates are online. Please use the most recent link on the authors' guide to find the files.

 

About TOMM

 

A peer-reviewed, quarterly archival journal in print and digital form, TOMM consists primarily of research papers of lasting importance and value in the field of multimedia computing, communications and applications. 

 

[October 2017]

2018/2019 SPECIAL ISSUE CALL 

 

News archive
Forthcoming Articles
Structure-Aware Deep Learning for Product Image Classification

Automatic product image classification is a task of crucial importance towards better understanding and management of online retailers. Motivated by recent advancements of deep convolutional neural network (CNN) on image classification, in this work we revisit the problem in the context of product images with the existence of a predefined categorical hierarchy and attributes, aiming to leverage the hierarchy and attributes to further improve the classification accuracy. With these structural-aware clues, we argue that more advanced CNN models could be developed beyond the one-versus-all classification as did by conventional CNNs. To this end, novel efforts of this work include: developing a salient-sensitive CNN that could focus more on the product foreground by inserting a spatial attention layer to a proper location, proposing a multi-class regression based refinement method that is expected to generate more accurate predictions by utilizing prediction scores from preceding multiple CNNs, each corresponding to a distinctive classifier on a categorical layer in the hierarchy, and devising a multi-task deep learning architecture that effectively explore correlations among the categories and attributes for better categorical label prediction. Experimental results on nearly one million real-world product images basically validate the effectiveness of the proposed efforts jointly and individually, from which performance gains are observed.

Dense 3D-Convolutional Neural Network for Person Re-Identification in Videos

Person re-identification aims at identifying a certain pedestrian across non-overlapping multi-camera networks in different time and places. Existing person re-identification approaches mainly focus on matching pedestrians on still images, however little attention is paid to person re-identification in videos. Compared to images, video clips contain motion of pedestrians which is crucial to re-identification. Moreover, consecutive video frames present pedestrian appearance with different poses and from different viewpoints, providing valuable information towards addressing the challenge of pose variation, occlusion, and viewpoint change etc. In this paper, we propose a Dense 3D-Convolutional Network (D3DNet) to jointly learn spatio-temporal and appearance features for person re-identification in videos. The D3DNet consists of multiple 3D dense blocks and transition layers. The 3D dense blocks enlarge the receptive fields of visual neurons in spatial and temporal dimensions, leading to discriminative appearance representation as well as short-term and long-term motion information of pedestrians without the requirement of an additional motion estimation module. Moreover, we propose an improved loss function consisting of identification loss and center loss to minimize intra-class variance and maximize inter-class variance simultaneously, towards addressing the challenge of large intra-class variance and small inter-class variance, which is common phenomenon in person re-identification task. Extensive experiments on two widely-used surveillance video datasets, i.e., MARS and iLIDS-VID, have shown the effectiveness of the proposed approach.

Deep Patch Representations with Shared Codebook for Scene Classification

Scene classification is a challenging problem. Compared with object images, scene images are more abstract, which are composed of objects. Object and scene images have different characteristics with different scales and composition structures. How to effectively integrate the local mid-level semantic representation including both object and scene concepts needs to be investigated, which is an important aspect for scene classification. In this paper, the idea of share codebook is introduced by organically integrating deep learning, concept feature and local feature encoding techniques. More specifically, the shared local feature codebook is generated from the combined ImageNet1000 and Places365 concepts (Mixed1365), using convolutional neural networks. As the mixed 1365 features covers all the semantic information including both object and scene concepts, we can extract shared codebook from the mixed 1365 features which only contains a subset of the whole 1365 concepts with the same codebooks size. The shared codebook can not only provide complementary representations without additional codebook training, but also it can be adaptively extracted towards different scene classification tasks. A method of combining both the original codebook and the shared codebook is proposed for scene classification. In this way, more comprehensive and representative image features can be generated for classification. Extensive experimentations conducted on two public dataset validate the effectiveness of the proposed method. Besides, some useful observations are also revealed to show the advantage of shared codebook.

Efficient QoE-Aware Scheme for Video Quality Switching Operations in Dynamic Adaptive Streaming

Dynamic Adaptive Streaming over HTTP (DASH) is a popular over-the-top video content distribution technique that adapts the streaming session according to the users network condition typically in terms of downlink bandwidth. This video quality adaptation can be achieved by scaling the frame quality, spatial resolution or frame rate. Despite the flexibility on the video quality scaling methods, each of these quality scaling dimensions has varying effects on the Quality of Experience (QoE) for end users. Furthermore, in video streaming, the changes in motion over time along with the scaling method employed have an influence on QoE, hence the need to carefully tailor scaling methods to suit streaming applications and content type. In this work, we investigate an intelligent DASH approach for the latest video coding standard H.265 and propose a heuristic QoE-aware cost-efficient adaptation scheme that does not switch unnecessarily to the highest quality level but rather stays temporarily at an intermediate quality level in certain streaming scenarios. Such an approach achieves a comparable and consistent level of quality under impaired network conditions as commonly found in Internet and mobile networks whilst reducing bandwidth requirements and quality switching overhead. The rationale is based on our empirical experiments, which show that an increase in bitrate does not necessarily mean noticeable improvement in QoE. Furthermore, our work demonstrates that the Signal-to-Noise Ratio (SNR) and the spatial resolution scalability types are the best fit for our proposed algorithm. Finally, we demonstrate an innovative interaction between quality scaling methods and the polarity of switching operations. The proposed QoE-aware scheme is implemented and empirical results show that it is able to reduce bandwidth requirements by up to 41% whilst achieving equivalent QoE compared with a representative DASH reference implementation.

Robust Electric Network Frequency Estimation with Rank Reduction and Linear Prediction

This paper deals with the problem of electric network frequency (ENF) estimation where signal to noise ratio (SNR) is an essential challenge. By exploiting the low-rank structure of the ENF signal from the audio spectrogram, we propose an approach based on robust principle component analysis to get rid of the interference from speech contents and some of the background noise, which in our case can be regarded as sparse in nature. Weighted linear prediction is enforced on the low-rank signal subspace to gain accurate ENF estimation. The performance of the proposed scheme is analyzed and evaluated as a function of SNR, and the Cramér-Rao lower bound (CRLB) is approached at an SNR level above -10dB. Experiments on real datasets have demonstrated the advantages of the proposed method over the state-of-the-art works in terms of estimation accuracy. Specifically, the proposed scheme can effectively capture the ENF fluctuations along the time axis using small number of signal observations while preserving sufficient frequency precision.

SKEPRID: Pose and Illumination Change-Resistant Skeleton-Based Person Re-Identification

Currently, the surveillance camera-based person re-identification is still challenging because of diverse factors such as peoples changing poses and various illumination. The various poses make it hard to conduct feature matching across images, and the illumination changes make color-based features unreliable. In this paper, we present SKEPRID1, a skeleton-based person re-identification method which handles strong pose and illumination changes jointly. To reduce the impacts of pose changes on re-identification, we estimate the joints positions of a person based on the deep learning technique, and thus make it possible to extract features on specific body parts with high accuracy. Based on the skeleton information, we design a set of local color comparison-based cloth type features, which are resistant to various lighting conditions. Moreover, to better evaluate SKEPRID, we build the PO&LI2 dataset, which has large pose and illumination diversity. Our experimental results show that SKEPRID outperforms state-of-the-art approaches in the case of strong pose and illumination variation.

Visual Content Recognition by Exploiting Semantic Feature Map with Attention and Multi-task Learning

Recent studies have shown that spatial relationships among objects are very important for visual recognition since they provide rich clues on object contexts within images. In this paper, we introduce a novel method to learn Semantic Feature Map (SFM) with attention based deep neural networks for image and video classification in an end to end manner, with an aim to explicitly model spatial object contexts within the images. In particular, for every object proposals obtained from the input image, we extract high-level semantic object features with convolutional neural networks. Then, we explicitly apply gate units to these extracted features for important objects selection and noise removal. These selected object features are organized into the proposed SFM, which is a compact and discriminative representation with the spatial information among objects preserved. Finally, we employ either Fully Convolutional Networks (FCN) or Long-Short Term Memory (LSTM) as classifiers on top of the SFM for content recognition, which are expected to exploit the spatial relationships among objects. We also introduce a novel multi-task learning framework to help learn the model parameters in the training phase. It consists of a basic image classification loss in cross entropy form, an object localization loss to guide important object selection, as well as a grid labeling loss to predict object labels at SFM grids. We conduct extensive evaluations and comparative studies to verify the effectiveness of the proposed approach and very promising results are obtained on Pascal VOC 2007/2012 and MS-COCO benchmarks for image classification. In addition, the SFMs learned on the image domain are transferred to video classification on CCV and FCVID benchmarks and the results successfully demonstrate its robustness and generalization capability.

Collaborations on YouTube: From Unsupervised Detection to the Impact on Video and Channel Popularity

YouTube is one of the most popular platforms for streaming of user-generated video. Nowadays, professional YouTubers have organized in so called multi-channel networks (MCNs). These networks offer services like brand deals, equipment, and strategic advice in exchange for a share of the YouTubers' revenue. A major strategy to gain more subscribers and, hence, revenue is collaborating with other YouTubers. Yet, collaborations on YouTube have not been studied in a detailed quantitative manner. This paper aims to close this gap. Therefore, we state three contributions. First, we collect a YouTube dataset covering video statistics over three months for 7,942 channels. Second, we design a framework for detection on an previously unknown number of persons in videos for analysis of collaborations in YouTube videos using a Deep Neural Network (DNN) based approach, named CATANA. Third, we analyze about seven years of video content and use CATANA to answer research questions providing guidance for YouTubers and MCNs for efficient collaboration strategies. Thereby, we focus on collaboration frequency and partner selectivity, (ii) the influence of MCNs on channel collaborations, (iii) collaborating channel types, and (iv) the impact of collaborations on video and channel popularity. Our results show that collaborations are in many cases significantly positive for the collaborating channels showing often more than 100% popularity growth compared with non-collaboration videos.

Unsupervised Person Re-identification: Clustering and Fine-tuning

The superiority of deeply learned pedestrian representations has been reported in very recent literature of person re- identification (re-ID). In this paper, we consider the more pragmatic issue of learning a deep feature with no or only a few labels. We propose a progressive unsupervised learning (PUL) method to transfer pretrained deep representations to unseen domains. Our method is easy to implement and can be viewed as an effective baseline for unsupervised re-ID feature learning. Specifically, PUL iterates between 1) pedestrian clustering and 2) fine-tuning of the convolutional neural network (CNN) to improve the original model trained on the irrelevant labeled dataset. Since the clustering results can be very noisy, we add a selection operation between the clustering and fine-tuning. At the beginning when the model is weak, CNN is fine-tuned on a small amount of reliable examples which locate near to cluster centroids in the feature space. As the model becomes stronger in subsequent iterations, more images are being adaptively selected as CNN training samples. Progressively, pedestrian clustering and the CNN model are improved simultaneously until algorithm convergence. This process is naturally formulated as self-paced learning. We then point out promising directions that may lead to further improvement. Extensive experiments on three large-scale re-ID datasets demonstrate that PUL outputs discriminative features that improve the re-ID accuracy. Our code has been released at https://github.com/hehefan/Unsupervised-Person-Re-identification-Clustering-and-Fine-tuning.

Personalized Emotion Recognition by Personality-aware High-order Learning of Physiological Signals

Emotion recognition methodologies from physiological signals are increasingly becoming personalized, due to the subjective responses of different subjects to physical stimuli. Existing works mainly focused on modelling the involved physiological corpus of each subject, without considering the psychological factors, such as interest and personality. The latent correlation among different subjects has also been rarely examined. In this paper, we propose to investigate the influence of personality on emotional behavior in a hypergraph learning framework. Assuming that each vertex is a compound tuple (subject, stimuli), multi-modal hypergraphs can be constructed based on the personality correlation among different subjects and on the physiological correlation among corresponding stimuli. To model the different importance within vertices, hyperedges and modalities, we assign each of them with weight. Doing so allows the learning procedure to be conducted on the vertex-weighted multi-modal multi-task hypergraphs, thus simultaneously modelling the emotions of multiple subjects. The estimated emotion relevance is employed for emotion recognition. We carry out extensive experiments on the ASCERTAIN dataset and the results demonstrate the superiority of the proposed method, as compared to the state-of-the-art approaches.

Modeling Dyadic and Group Impressions with Inter-Modal and Inter-Person Features

This paper proposes a novel feature-extraction framework for inferring impressed personality traits, emergent leadership skills, communicative competence and hiring decisions. The proposed framework extracts multimodal features, describing each participant's nonverbal activities. It captures inter-modal and inter-person relationships in interaction and captures how the target interactor generates nonverbal behavior when the other interactors also generate the nonverbal behavior. The inter-modal and inter-personal patterns are identified as frequent co-occurring events based on graph clustering from multimodal sequences. The framework can be applied to any type of interaction task. The proposed framework is applied to the SONVB corpus, which is an audio-visual dataset collected from dyadic job interviews, and the ELEA audio-visual data corpus, which is a dataset collected from group meetings. We evaluate the framework on a binary classification task of 15 impression variables in two data corpora. The experimental results show that the model trained with co-occurrence features is more accurate than previous models for 14 out of 15 traits.

Cross-Modality Feature Learning via Convolutional AutoEncoder

Learning robust and representative feature across multiple modalities has been a fundamental problem in machine learning and multimedia fields. In this paper, we propose a novel MUltimodal Convolutional AutoEncoder (MUCAE) approach to learn representative features from visual and textual modalities. For each modality, we integrate the convolutional operation into an autoencoder framework to learn a joint representation from original image and text content. We optimize the convolutional autoencoders of different modalities jointly by exploiting the correlation between the hidden representations from the convolutional autoencoders, in particular by minimizing both the reconstructing error of each modality and the correlation divergence between the hidden feature of different modalities. Compared to the conventional solutions relying on hand-crafted features, the proposed MUCAE approach encodes features from image pixels and text characters directly and produces more representative and robust features. We evaluate MUCAE on cross media retrieval as well as unimodal classification tasks over real-world large-scale multimedia databases. Experimental results have shown that MUCAE performs better than the state-of-the-arts methods.

Applying Deep Learning to Epilepsy Seizure Detection and Brain Mapping

Deep convolution neural network (CNN) has achieved remarkable results in computer vision tasks for end-to-end learning. We evaluate here the power of a deep CNN to learn robust features from raw EEG data to detect seizures. Seizure are hard to detect as they vary both inter- and intra-patient. In this paper, we use a deep CNN model for seizure detection task on an open access EEG epilepsy dataset collected at the Childrens Hospital Boston. Our deep learning model is able to extract spectral, temporal features from EEG epilepsy data and use them to learn general structure of a seizure that is less sensitive to variations. Our method produced an overall sensitivity of 90.00 %, specificity of 91.65% and accuracy of 98.05% for whole dataset of 23 patients. Hence, it can be used as an excellent cross-patient classifier. The results show that our model performs better than previous state of the art models for cross-patient seizure detection task. The proposed model can also visualize special orientation of band power features. We use correlation maps to relate spectral amplitude features to the output in the form of images. By using the results from our deep learning model, this visualization method can be used as an effective multimedia tool for producing quick and relevant brain mapping images that can be used by medical experts for further investigation.

Deep Semantic Mapping for Heterogeneous Multimedia Transfer Learning Using Co-Occurrence Data

Transfer learning, which focuses on finding a favorable representation for instances of different domains based on auxiliary data, can mitigate the divergence between domains through knowledge transfer. Recently, increasing efforts on transfer learning have employed deep neural network (DNN) to learn more robust and higher level feature representations to better tackle cross-media disparity. However, only a few papers consider the correction and semantic matching between multi-layer heterogeneous domain networks. In this paper, we propose a deep semantic mapping model for heterogeneous multimedia transfer learning (DHTL) using co-occurrence data. More specifically, we integrate the DNN with canonical correlation analysis (CCA) to derive a deep correlation subspace as the joint semantic representation for associating data across different domains. In the proposed DHTL, a multi-layer correlation matching network across domains is constructed, in which the CCA is combined to bridge each pair of domain-specific hidden layers. To train the network, a joint objective function is defined and the optimization processes are presented. When the deep semantic representation is achieved, the shared features of the source domain are transferred for task learning in the target domain. Extensive experiments for three multimedia recognition applications demonstrate that the proposed DHTL can effectively find deep semantic representations for heterogeneous domains, and is superior to the several existing state-of-the-art methods for deep transfer learning.

Unsupervised Similarity Learning through Rank Correlation and kNN Sets

The increasing amount of multimedia data collections available today evinces the pressing need for methods capable of indexing and retrieving this content. Despite of the continuous advances in multimedia features and representation models, to establish an effective measure for comparing different multimedia objects still remains a challenging task. While supervised and semi-supervised techniques made relevant advances on similarity learning tasks, scenarios where labeled data is non-existent require different strategies. In such situations, unsupervised learning has been established as a promising solution, capable of considering the contextual information and the dataset structure for computing new similarity/dissimilarity measures. This paper extends a recent unsupervised learning algorithm which uses an iterative re-ranking strategy to take advantage of different kNN sets and rank correlation measures. Two novel approaches are proposed for computing the kNN sets and their corresponding top-k lists. The proposed approaches were validated in conjunction with various rank correlation measures, yielding superior effectiveness results in comparison with previous works. In addition, we also evaluate the ability of the method in considering different multimedia objects, conducting an extensive experimental evaluation on various image and video datasets.

A Network based Virtual Reality Simulation Training Approach for Orthopedic Surgery

The focus of this paper is on the adoption of a next generation internet and Virtual Reality (VR) technologies for the development of fully immersive and haptic simulators for training of medical residents in a surgical process termed Less Invasive Stabilization System (LISS) plating surgery. LISS surgery is an orthopaedic surgical process developed for healing of fractured femur bone. Development of such simulators is a complex task which involves multiple systems, technologies and human experts. Emerging Next Generation Internet technologies were used to develop the haptic based collaborative simulator. A standalone fully immersive surgical simulator was also developed using HTC Vive. Expert surgeons played an important role in developing the simulator system; use cases of the target surgical processes were built using a modeling language called the engineering Enterprise Modeling Language. The impact of the using the simulators has been explored through interactions with residents during multiple phases which underscores the potential of using such simulators in medical training.

Thinking Like a Director: Film Editing Patterns for Virtual Cinematographic Storytelling

This paper introduces Film Editing Patterns (FEP), a language to formalize film editing practices and stylistic choices found in movies. FEP constructs are constraints expressed over one or more shots from a movie sequence that characterize changes in cinematographic visual properties such as shot size, region, angle of on-screen actors. We first present the elements of the FEP language, then introduce its usage in annotated film data, and finally describe how it can support users in the creative design of film sequences in 3D, more specifically: (i) we present an application to craft edited filmic sequences from 3D animated scenes that uses FEPs to support the user in selecting camera framings and editing choices that follow certain best practices used in cinema; (ii) we conduct an evaluation of the application with professional and non-professional filmmakers. The evaluation suggests that users generally appreciate the idea of FEP, and that it can effectively help novice and medium experienced users in crafting film sequences with little training and satisfying results.

Convolutional Attention Networks for Scene Text Recognition

In this paper, we present convolutional attention networks (CAN) for unconstrained scene text recognition. Recent dominant approaches for scene text recognition are mainly based on convolutional neural networks (CNN) and recurrent neural networks (RNN), where the CNN encodes images and the RNN generates character sequences. Our CAN is different from these methods, which is completely built on CNN and combines attention mechanism. The distinctive characteristics of our method include: (1) CAN follows encoder-decoder architecture, in which the encoder is a deep two-dimensional CNN and the decoder is a one-dimensional CNN. (2) The attention mechanism is applied in every convolutional layer of the decoder, and we propose a novel spatial attention method using average pooling. (3) Position embeddings are equipped in both spatial encoder and sequence decoder to give our networks a sense of locations. We conduct experiments on standard datasets for scene text recognition, including Street View Text, IIIT5K and ICDAR datasets. The experimental results validate the effectiveness of different components, and show our convolutional-based method achieves state-of-the-art or competitive performance than prior works, even without the use of RNN.

Joint Head Attribute Classifier and Domain-Specific Refinement Networks for Face Alignment

In this paper, a two-stage refinement network is proposed for facial landmarks detection on unconstrained conditions. Our model can be divided into two modules, namely Head Attitude Classification (HAC) module and Domain-Specific Refinement (DSR) module. Given an input facial image, HAC adopts multi-task learning mechanism to detect the head pose and obtain an initial shape. Based on the obtained head pose, DSR designs three different CNN-based refinement networks trained by specific domain respectively, and automatically selects the most approximate network for the landmarks refinement. In our framework, HAC combines head pose classification with facial landmarks detection to improve the accuracy of head pose estimation, as well as obtaining a robust initial shape. Moreover, an adaptive sub-network training strategy applied in the DSR module can strongly solve the issue of traditional multi-view methods that an improperly selected sub-network may result in alignment failure. The extensive experimental results on two public datasets, AFLW and 300W, confirm the validity of our model.

Probability model-based early Merge mode decision for dependent views coding in 3D-HEVC

As a 3D extension to High Efficiency Video Coding (HEVC) standard, 3D-HEVC is developed to improve the coding efficiency of multi-view videos. It inherits the prediction modes from HEVC, yet both motion estimation (ME) and disparity estimation (DE) are required for the coding of dependent views. This improves coding efficiency at the cost of huge computational costs. In this paper, an early Merge mode decision approach is proposed for dependent texture views and dependent depth maps coding in 3D-HEVC based on priori posterior probability model. Firstly, the priori probability model is established by exploiting the hierarchical and inter-view correlations from those previously encoded blocks. Secondly, the posterior probability model is builded by using the coded block flag (CBF) of the current coding block. Finally, the joint priori and posterior probability model is adopted to early terminate Merge mode decision for both dependent texture views and dependent depth maps coding. Experimental results show that the proposed approach saves 45.2% and 30.6% encoding time on average for dependent texture views and dependent depth maps coding while maintaining negligible loss of coding efficiency, respectively.

A Hybrid Approach for Spatio-temporal Validation of Declarative Multimedia Documents

Declarative multimedia documents represent the description of multimedia applications in terms of media items and relationships among them. Relationships specify how media items are dynamically arranged in time and space during runtime. Although a declarative approach usually facilitates the authoring task, authors can still make mistakes due to incorrect use of language constructs or inconsistent or missing relationships in a document. In order to properly support multimedia application authoring, it is important to provide tools with validation capabilities. Document validation can indicate possible inconsistencies in a given document to an author, so that it can be revised before deployment. Although very useful, multimedia validation tools are not often provided by authoring tools. This work proposes a multimedia validation approach that relies on a formal model, called Simple Hypermedia Model (SHM). SHM is used for representing a document for the purpose of validation. An SHM document is validated using a hybrid approach based on two complimentary techniques. The first one captures the document spatio-temporal layout in terms of its state throughout its execution by means of a rewrite theory and validation is performed through model-checking. The second one captures the document layout in terms of intervals and event occurrences by means of SMT (Satisfiability Modulo Theories) formulas and validation is performed through SMT solving. Due to different characteristics of both approaches, each validation technique complements the other in terms of expressiveness of SHM and tests to be checked. We briefly present validation tools that use our approach. They were evaluated with real NCL and web documents and by usability tests.

Bibliometrics

Publication Years 2005-2018
Publication Count 629
Citation Count 3456
Available for Download 628
Downloads (6 weeks) 2912
Downloads (12 Months) 27912
Downloads (cumulative) 269623
Average downloads per article 429
Average citations per article 5
First Name Last Name Award
Ruzena R Bajcsy ACM Distinguished Service Award (2003)
ACM AAAI Allen Newell Award (2001)
ACM Fellows (1996)
Susanne Boll ACM Senior Member (2012)
Surendar Chandra ACM Senior Member (2009)
Shih Fu Chang ACM Fellows (2017)
Kuan-Ta Chen ACM Senior Member (2015)
Matthew L Cooper ACM Distinguished Member (2016)
ACM Senior Member (2010)
Jon Crowcroft ACM Fellows (2002)
Alberto Del Bimbo ACM Distinguished Member (2016)
Abdulmotaleb El Saddik ACM Distinguished Member (2010)
ACM Senior Member (2008)
Claudio A. Feijoo ACM Senior Member (2009)
Wen Gao ACM Fellows (2013)
Shahram Ghandeharizadeh ACM Software System Award (2008)
Soheil Ghiasi ACM Senior Member (2015)
Giorgio Giacinto ACM Senior Member (2010)
Andreas Girgensohn ACM Distinguished Member (2008)
Michael L Gleicher ACM Distinguished Member (2011)
Tracy Anne Hammond ACM Senior Member (2015)
Lynda Hardman ACM Distinguished Member (2014)
ACM Senior Member (2013)
Xian-Sheng Hua ACM Distinguished Member (2015)
ACM Senior Member (2009)
Tiejun Huang ACM Senior Member (2013)
Ramesh C Jain ACM Fellows (2003)
Wessel Kraaij ACM Distinguished Member (2017)
ACM Senior Member (2007)
James Kurose ACM Fellows (2001)
Ming Li ACM Fellows (2006)
Saverio Mascolo ACM Senior Member (2009)
Tao Mei ACM Distinguished Member (2016)
ACM Senior Member (2012)
Filippo Menczer ACM Distinguished Member (2013)
Saraju P. Mohanty ACM Senior Member (2010)
Klara Nahrstedt ACM Fellows (2012)
Nuria Oliver ACM Fellows (2017)
ACM Distinguished Member (2015)
ACM Senior Member (2013)
Dan R Olsen ACM Fellows (2006)
Beng Chin Ooi ACM Fellows (2011)
Ming Ouhyoung ACM Senior Member (2007)
Sethuraman Panchanathan ACM Senior Member (2009)
K. K. Ramakrishnan ACM Fellows (2017)
Christoph Rensing ACM Senior Member (2018)
Joel Jose Rodrigues ACM Senior Member (2011)
Keith Ross ACM Fellows (2012)
Lawrence A Rowe ACM Fellows (1998)
Yong Rui ACM Fellows (2017)
ACM Distinguished Member (2009)
ACM Senior Member (2006)
Michael Rung-Tsong Lyu ACM Fellows (2015)
Mahadev Satyanarayanan ACM Software System Award (2016)
ACM Fellows (2002)
Henning Schulzrinne ACM Fellows (2014)
David Ayman Shamma ACM Distinguished Member (2016)
ACM Senior Member (2011)
Prashant J Shenoy ACM Distinguished Member (2009)
ACM Senior Member (2006)
Frank Shipman ACM Distinguished Member (2009)
Shervin Shirmohammadi ACM Senior Member (2017)
Malcolm Slaney ACM Senior Member (2018)
Ralf Steinmetz ACM Fellows (2001)
Richard Szeliski ACM Fellows (2008)
Bart Thomee ACM Senior Member (2016)
Donald F Towsley ACM Fellows (1997)
Matthew A Turk ACM Senior Member (2007)
Benjamin W. Wah ACM Fellows (2004)
Jingdong Wang ACM Senior Member (2014)
Shuicheng Yan ACM Distinguished Member (2016)
HongJiang Zhang ACM Fellows (2007)
Hui Zhang ACM Fellows (2005)
Lei Zhang ACM Senior Member (2011)
Michelle Zhou ACM Distinguished Member (2009)
ACM Senior Member (2007)
Roger Zimmermann ACM Distinguished Member (2017)

First Name Last Name Paper Counts
Changsheng Xu 15
Tatseng Chua 14
Shuicheng Yan 14
Mohamed Hefeeda 14
Mohan Kankanhalli 11
Weitsang Ooi 10
Klara Nahrstedt 10
Chenghsin Hsu 9
Shervin Shirmohammadi 8
Pradeep Atrey 8
James She 8
Roger Zimmermann 8
Gheorghita Ghinea 8
Tao Mei 7
Ralf Steinmetz 7
Carsten Griwodz 7
Yong Rui 7
Qi Tian 6
Pål Halvorsen 6
Namunu Maddage 5
Jiangchuan Liu 5
Svetha Venkatesh 5
Abdulmotaleb El Saddik 5
Pablo César 5
Mohammad Hossain 5
Balakrishnan Prabhakaran 5
Shihfu Chang 5
Wolfgang Effelsberg 5
Meng Wang 5
Abdulmotaleb El Saddik 5
Hari Sundaram 5
Jitao Sang 5
Ming Cheung 5
Changwen Chen 5
Zongpeng Li 4
Dick Bulterman 4
Shipeng Li 4
Xiansheng HUA 4
Shueng Chan 4
Alberto Del Bimbo 4
Richang Hong 4
Ramesh Jain 4
Gabriel Muntean 4
Michael Zink 4
Oluwakemi Ademoye 4
Marcel Worring 3
Romulus Grigoraş 3
Géraldine Morin 3
Prashant Shenoy 3
Laurencetianruo Yang 3
Stephan Kopf 3
Séamus McLoone 3
Zhengjun Zha 3
Eckehard Steinbach 3
Tomás Ward 3
Nabil Sarhan 3
Stefano Berretti 3
Gwendal Simon 3
Jinhui Tang 3
WeiQi Yan 3
Alan Hanjalic 3
Rongrong Ji 3
Jiwu Huang 3
Wenwu Zhu 3
Ruzena Bajcsy 3
Rui Yang 3
Si Liu 3
Zheng Yan 3
Yong Rui 3
Mohamad Eid 3
Songqing Chen 3
Mark Claypool 3
Baochun Li 3
Kien Hua 3
Niall Murray 3
Chuan Wu 3
Kuanta Chen 3
Robert Deng 3
Luming Zhang 3
Alexandru Iosup 3
Guojun Qi 3
Gregorij Kurillo 3
Ketan Mayer-Patel 3
Lawrence Rowe 2
Michael Pearce 2
Venugopal Vasudevan 2
Francesco De Natale 2
Maha Abdallah 2
Linjun Yang 2
Qingchen Zhang 2
Indranil Gupta 2
Bin Cheng 2
Ramesh Jain 2
Xue Li 2
Anup Basu 2
Djamila Aouada 2
Aisling Kelliher 2
Susanne Boll 2
Xiaoshan Yang 2
Brett Adams 2
Yifang Yin 2
Kasim Candan 2
Ming Li 2
Haizhou Li 2
Bo Shen 2
Stefan Wilk 2
Zechao Li 2
Dag Johansen 2
Thomas Zinner 2
Amr Rizk 2
Jie Yang 2
Wolfgang Kellerer 2
Jun Ye 2
Andreas Girgensohn 2
Lynn Wilcox 2
Houqiang Li 2
Wei Cheng 2
Sabu Emmanuel 2
Jiwu Huang 2
Wuchi Feng 2
Rynson Lau 2
Zhi Wang 2
Jia Li 2
Lea Skorin-Kapov 2
Jinjun Chen 2
Wanmin Wu 2
Houqiang Li 2
Rahul Potharaju 2
Mubarak Shah 2
Yao Liu 2
Steven Hoi 2
Huanbo Luan 2
Gerald Friedland 2
Matthias Baldauf 2
Yuru Lin 2
Xiaofei He 2
Nicu Sebe 2
Munchoon Chan 2
Xu Cheng 2
Shiqiang Yang 2
Kien Hua 2
Chusong Chen 2
Zhenyu Yang 2
Hefei Ling 2
Hendrik Knoche 2
Robert Kinicki 2
Xi Zhou 2
Weiqi Luo 2
Nicola Conci 2
Jongeun Cha 2
Vamsidhar Gaddam 2
Ragnar Langseth 2
Zhikui Chen 2
Min Song 2
Xiangyu Wang 2
Yiping Hung 2
Vincent Oria 2
Ahsan Arefin 2
Matti Siekkinen 2
Liang Zhou 2
Phuoc Tran-Gia 2
Michael Gleicher 2
Hongjiang ZHANG 2
Geoff West 2
Mohan Kankanhalli 2
Bing Wang 2
Don Towsley 2
Lifeng Sun 2
Chunying Huang 2
Liang Chen 2
Xun Yang 2
Kiana Calagari 2
Deng Cai 2
Stefano Petrangeli 2
Filip De Turck 2
David Shamma 2
Peng Cui 2
Yun Fu 2
Sebastien Mondet 2
Stephen Gulliver 2
Zhenhua Li 2
Min Xu 2
Hai Jin 2
Xiaopeng Li 2
Jiunlong Huang 2
Siqi Shen 2
Ming Yan 2
Jordi Batalla 2
Lawrence Rowe 2
Yiliang Zhao 2
Yipeng Zhou 2
Pascal Frossard 2
Yeongju Lee 2
Hanqing Lu 2
Michael Houle 2
Jichao Sun 2
Francesco De Natale 2
Dacheng Tao 2
Xin Zhang 2
Mohamed Daoudi 2
Pavan Turaga 2
Daniel Gatica-Perez 2
Xiaobai Liu 2
Simon Moncrieff 2
Yugang Jiang 2
Chongwah Ngo 2
Xi Shao 2
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Peiyu Lin 2
Jiebo Luo 2
Christian Timmerer 2
Yuansong Qiao 2
Zhenwei Zhao 2
Shuqiao Zhao 2
Meng Wang 2
Arijit Sur 2
Bineng Zhong 2
Chongwah Ngo 2
Björn Ottersten 2
Divyashri Bhat 2
Derek Eager 2
Jesse Jin 2
Keqiu Li 2
Ajay Gopinathan 2
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Gang Hua 2
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Michael Needham 2
Wei Liu 2
Jamesze Wang 2
Azzedine Boukerche 2
Surendar Chandra 2
Sakirearslan Ay 1
Senching Cheung 1
Chihyi Chiu 1
Nimesha Ranasinghe 1
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Siqing Zheng 1
Yulei Wu 1
Seungmin Rho 1
Nei Kato 1
Tsunghan Tsai 1
Richardtianbai Ma 1
Khechai Sim 1
João Gomes 1
Ramesh Jain 1
Flora Li 1
Ekaterina Gonina 1
Penporn Koanantakool 1
John Boreczky 1
Thinh Nguyen 1
Dian Tjondronegoro 1
Bir Bhanu 1
Yucyuan Liou 1
Manoranjan Dash 1
Ziad Sakr 1
Liwei Kuang 1
Kai Qiu 1
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Saleh Almowuena 1
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Mohammad Hoque 1
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Ke Li 1
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Min Tan 1
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Ed Kaiser 1
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Song Ci 1
Shu Shi 1
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Jiajun Bu 1
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Liqi Shi 1
Jinsoo Kim 1
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Ashkan Sobhani 1
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Xiao Xu 1
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Hai Jin 1
Ryan Spicer 1
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Fei Hao 1
Geyong Min 1
Victor Leung 1
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Longyu Zhang 1
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Song Tan 1
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Bin Song 1
Honggang Wang 1
Chinghsien Hsu 1
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Cong Wang 1
Keke Gai 1
Zhong Ming 1
Jinhui Tang 1
Kanav Kahol 1
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Troy McDaniel 1
Ramon Aparicio-Pardo 1
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Norman Murray 1
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Liang Lin 1
Balasubramanian Raman 1
Meng Wang 1
Sergio Benini 1
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Ruchira Naskar 1
Howard Leung 1
Vincent Charvillat 1
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Pedro Inácio 1
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Yadong Mu 1
Bo Lang 1
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Weita Chu 1
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Zhangyu Chang 1
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Jun Mi 1
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Mi Jing 1
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Haibo Chen 1
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Wolfgang Klas 1
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Junehwa Song 1
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Yadong Mu 1
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Philippe Bertin 1
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Hao Zhang 1
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Linxie Tang 1
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Brian Lee 1
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S Chan 1
Liqiang Nie 1
Haakon Riiser 1
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Huahui Wu 1
Yicheng Tu 1
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Meng Xing 1
Ming Dong 1
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Yunhong Wang 1
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Yi Zhu 1
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Tatjen Cham 1
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Michael Wallick 1
Howard Wactlar 1
Dan Tsafrir 1
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Gang Peng 1
Lan Huang 1
Ante Jurčević 1
Mark Hendrikx 1
Yantao Zheng 1
Chenghsin Hsu 1
Robert Pless 1
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Peng Li 1
Hassan Omar 1
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Joonwon Lee 1
Qingfang Zheng 1
Frederic Thouin 1
Yandong Tang 1
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Carlo Fantozzi 1
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Richard Han 1
Qin Lv 1
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Ulrich Newmann 1
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Felix Yu 1
Derek Bunn 1
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Lei Pang 1
Mulin Chen 1
Qi Wang 1
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Zibin Wang 1
Simone Bianco 1
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Xue Liu 1
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Ligang Zheng 1
Lexing Xie 1
Jian Cheng 1
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Alexander Hauptmann 1
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William Seager 1
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Stefano D'Aronco 1
Luca De Cicco 1
Piotr Krawiec 1
Bo Yang 1
Chris Bleakley 1
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W Culbertson 1
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Jing Liu 1
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Weiwei Xu 1
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Feng Liu 1
Prakash Kolan 1
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Lianqing Liu 1
Haibin Yu 1
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Hanhui Li 1
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Roy Campbell 1
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Nishanth Sastry 1
Bo Zhang 1
Jongtack Jung 1
Ali El Essaili 1
Jaegeuk Kim 1
Bharat Bhargava 1
Minoru Nakayama 1
Frédéric Boudon 1
Yong Wei 1
Heng Liu 1
Lei Zhang 1
Rainer Lienhart 1
Guillermo Cisneros 1
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Touradj Ebrahimi 1
Zhihan Lv 1
Marcella Cornia 1
Matti Nelimarkka 1
Daniel Johnson 1
Minjun Li 1
Wei Hu 1
Farid Melgani 1
Mauricio Orozco 1
Hengtao Shen 1
Yuli Gao 1
Xiangyang Xue 1
Yu Zhang 1
Jaling Wu 1
Yang Li 1
Shihchia Huang 1
Prasant Mohapatra 1
Tsungnan Lin 1
Ingmar Franke 1
Yowon Jeong 1
Xueyan Tang 1
Brandon Amos 1
Kanchan Bahirat 1
Balakrishnan Prabhakaran 1
Björn Jónsson 1
Michael Franklin 1
Kadangode Ramakrishnan 1
Zhi Wang 1
Bowen Ding 1
Yipeng Zhou 1
Hao Yin 1
Chuang Lin 1
Harry Agius 1
Mário Freire 1
Paulo Monteiro 1
Michelle Zhou 1
Yalin Lee 1
Michael Driscoll 1
Kurt Keutzer 1
Fei Li 1
Francis Lau 1
Jiangchuan Liu 1
Ming Ouhyoung 1
Kahphooi Seng 1
Shuhui Jiang 1
Yue Wu 1
Kuangyu Chang 1
Peisong Wang 1
Rynson Lau 1
Jiqing Wen 1
Hui Mao 1
Yuanyan Tang 1
Tony Sun 1
Hong Shen 1
Chihyi Chiu 1
Chinchen Chang 1
Thanh Dang 1
Ankita Lathey 1
Qianqian Hu 1
Shengzhong Feng 1
Mao Ye 1
Hui Xu 1
Zhenhui Yuan 1
Amir Mazaheri 1
Divya Singhal 1
Giuseppe Serra 1
Dongying Gong 1
Dan Ventura 1
Xi Wang 1
Jiwu Huang 1
Julio Valdés 1
Yi Yang 1
Shelley Buchinger 1
Prabhu Natarajan 1
Jyh Jang 1
Dilip Krishnappa 1
Bohao Chen 1
Bingkun Bao 1
Yusen Li 1
Yao Lu 1
Sujit Dey 1
Junjue Wang 1
Haitian Pang 1
Guangling Hou 1
Xiang Li 1
Feng Qiu 1
Jiebo Luo 1
Evangelos Georganas 1
Dongyu Liu 1
Jianming Lv 1
Zhenhua Li 1
Elaine Chew 1
Adrien Joly 1
Hanwang Zhang 1
Alvin Junus 1
Adam Wolisz 1
Xiongkuo Min 1
Tanima Dutta 1
Jingxi Xu 1
Herngyow Chen 1
Surong Wang 1
Jia Hu 1
Jiajia Liu 1
Hirotaka Ujikawa 1
Chunhua Hu 1
Mengbai Xiao 1
Xin Li 1
Chonggang Wang 1
Ruyan Wang 1
Fanyu Bu 1
Jun Liu 1
Yongdong Wu 1
Xiangbo Shu 1
Anne Tchounikine 1
Tyler Ballast 1
Shannon Chen 1
Nacim Ihadaddene 1
Paul Dickerson 1
Mika Tuomola 1
Terence Wright 1
Yong Rui 1
Mohan Kankanhalli 1
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Kazuya Sakai 1
Mikel Ariz 1
Tam Nguyen 1
John Gilmore 1
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João Cardoso 1
Olga Goussevskaia 1
Adlen Ksentini 1
Radu Mariescu-Istodor 1
Soheil Ghiasi 1
Edmond Ho 1
Håkon Stensland 1

Affiliation Paper Counts
National University of Tainan Taiwan 1
National Changhua University of Education 1
University of Minnesota Duluth 1
Samsung Electronics, India Software Operations Ltd. 1
Austrian Institute of Technology 1
Eindhoven University of Technology 1
University of California, Merced 1
University of Rochester 1
Huawei Technologies Co., Ltd. 1
Reykjavik University 1
Air Force Research Laboratory Information Directorate 1
University of Zurich 1
University of Surrey 1
The University of North Carolina Wilmington 1
University of the Basque Country 1
University of Pittsburgh 1
Adobe Systems Incorporated 1
Guangzhou University 1
Royal Institute of Technology 1
Saarland University 1
IMT Atlantique 1
University of Chicago 1
Beijing University of Technology 1
Google Inc. 1
Florida Institute of Technology 1
Harvard University 1
UNESP-Universidade Estadual Paulista 1
Deakin University 1
University of Windsor 1
Incheon National University 1
Muroran Institute of Technology 1
Center For Research And Technology - Hellas 1
Singapore University of Technology and Design 1
University of Edinburgh 1
Sam Houston State University 1
National Taiwan Ocean University 1
Southeast University China, Nanjing 1
China Telecommunications 1
University of Kuwait 1
GE Global Research 1
Macau University of Science and Technology 1
Hohai University 1
Hong Kong Polytechnic University 1
National University of Defense Technology China 1
EXFO 1
Carleton University 1
General Hospital of People's Liberation Army 1
University of Kent 1
Fuzhou University 1
National Taiwan Normal University 1
Kent State University 1
Henan University 1
Zhejiang Wanli University 1
Hongik University 1
Bowie State University 1
Johns Hopkins University 1
MIT Media Laboratory 1
National Chengchi University 1
INRIA Institut National de Rechereche en Informatique et en Automatique 1
Pontifical Catholic University of Rio de Janeiro 1
Intel Corporation 1
University of Coimbra 1
Clemson University 1
University of Ontario Institute of Technology 1
Institute of High Performance Computing, Singapore 1
University of Northumbria 1
Institut National des Telecommunications 1
University of South Carolina 1
Advanced Telecommunications Research Institute International (ATR) 1
Yarmouk University 1
Indian Institute of Technology Roorkee 1
Sungkyul Christian University 1
Cisco Systems 1
HEC School of Management 1
South Dakota School of Mines & Technology 1
Yale University 1
EURECOM Ecole d'Ingenieurs & Centre de Recherche en Systemes de Communication 1
IT University of Copenhagen 1
Jimei University 1
York University Canada 1
Dalian Maritime University 1
Ecole Centrale de Lyon 1
Japan National Institute of Information and Communications Technology 1
California State University Los Angeles 1
Agder University College 1
Malmo University 1
Laboratoire d'Automatique, Genie Informatique et Signal 1
University of Wales Trinity Saint David 1
National Central University Taiwan 1
University of Duisburg-Essen 1
University of Teesside 1
Tohoku University 1
Guangdong University of Petrochemical Technology 1
Cairo University 1
Uppsala University 1
Waterford Institute of Technology 1
National Taichung University of Science and Technology 1
University of Nebraska - Lincoln 1
CIRAD 1
University of Missouri System 1
Hebei Academy of Sciences 1
Indiana University 1
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Federal University of Sao Carlos 1
University of Canberra 1
CSIRO Data61 1
IMT Lille Douai 1
Open University 1
University of Pennsylvania 1
Sybase Inc. 1
Cancer Registry of Norway Institute of Population-Based Cancer Research 1
Dresden University of Applied Sciences 1
National Institute of Technology Kurukshetra 1
Baerum Hospital 1
University of Tokyo 1
University of Delaware 1
Silesian Polytechnic University, Gliwice 1
Ca' Foscari University of Venice 1
Hebei University of Technology 1
University of Massachusetts Boston 1
China Agricultural University 1
Saint Petersburg National Research University of Information Technologies, Mechanics and Optics University ITMO 1
Thapar University 1
University of Kentucky 1
IMAG 1
TELECOM ParisTech 1
University of Bielefeld 1
FHS St. Gallen University of Applied Sciences 1
Orange Labs 1
Microsoft Technology Centers 1
University of Massachusetts Dartmouth 1
IBM China Company Limited 1
University of Peshawar 1
New Mexico Institute of Mining and Technology 1
University of Qatar 1
Cornell University 1
University of Washington, Seattle 1
Nanchang Hangkong University 1
Auburn University 1
Macquarie University 1
British Broadcasting Corporation 1
Institut Dalle Molle D'intelligence Artificielle Perceptive 1
Institute of Information Science Academia Sinica Taiwan 1
King's College London 1
Tata Consultancy Services India 1
Guangdong Polytechnic Normal University 1
Ericsson Nikola Tesla d d 1
Karolinska University Hospital 1
NYU Tandon School of Engineering 1
Hunan University of Commerce 1
Universidad de Granada 1
Chung Hua University 1
IBM Canada Ltd. 1
Vienna University of Technology 1
Laboratoire de Biometrie et Biologie Evolutive, Villeurbanne 1
North Georgia College & State University 1
Eastman Kodak Company 1
Nanjing University of Information Science and Technology 1
Kansas State University 1
National Research Council Canada 1
Indian Institute of Technology Rajasthan 1
Federal University of Bahia 1
University of Tehran 2
Australian National University 2
University of Reading 2
Massachusetts Institute of Technology 2
Institut National de la Recherche Scientifique 2
Sharif University of Technology 2
Masaryk University 2
Polytechnic Institute of Turin 2
National Chi Nan University 2
Indian Institute of Technology, Kharagpur 2
Kodak Research Laboratories 2
Tongji University 2
RMIT University 2
International Computer Science Institute 2
Jaypee Institute of Information Technology University 2
Charles Sturt University, Wagga Wagga 2
Goldsmiths, University of London 2
DoCoMo Communications Laboratories Europe GmbH 2
University of Cincinnati 2
National Institute of Telecommunications, Poland 2
University Michigan Ann Arbor 2
Yuan Ze University 2
Instituto de Telecomunicacoes 2
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University of Regina 2
University of Durham 2
University of Adelaide 2
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University of Oldenburg 2
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Leiden University 2
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University of Milan - Bicocca 2
INSA Lyon 2
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Indraprastha Institute of Information Technology Delhi 2
Communication University of China 2
Hong Kong Baptist University 2
IBM Almaden Research Center 2
Federal University of Rio Grande do Sul 2
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture 2
Xi'an Jiaotong University 2
University of Houston 2
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Swinburne University of Technology 2
Queensland University of Technology 2
Indian Institute of Technology (Banaras Hindu University) 2
University of Sciences and Technology Houari Boumediene 2
National Cheng Kung University 2
The University of North Carolina at Charlotte 2
The University of Georgia 2
University of Nottingham 2
Roehampton University 2
University of Antwerp 2
National Chung Cheng University 2
Feng Chia University 2
Utrecht University 2
Tokyo Institute of Technology 2
Institute for Research in IT and Random Systems 2
Ohio State University 2
University of Stellenbosch 2
Texas State University-San Marcos 2
Washington University in St. Louis 2
University of Waterloo 2
Pace University 2
Telecom Research Center Vienna 2
Lund University 2
Oldenburger Forschungs- Und Entwicklungsinstitut fur Informatik-Werkzeuge Und -Systeme 2
Netherlands Organisation for Applied Scientific Research - TNO 2
Japan Advanced Institute of Science and Technology 2
Johannes Kepler University Linz 2
VMware, Inc 2
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Research Organization of Information and Systems National Institute of Informatics 3
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Simula Research Laboratory 4
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Telefonica 4
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Telecom Bretagne 5
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Institute of Computing Technology Chinese Academy of Sciences 6
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Microsoft Corporation 6
Binghamton University State University of New York 6
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University of Luxembourg 6
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Brigham Young University 6
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National Chiao Tung University Taiwan 6
Shenyang Institute of Automation Chinese Academy of Sciences 7
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Nanjing University of Science and Technology 7
Athlone Institute of Technology 7
Nanjing University of Post and TeleCommunications 7
University of Winnipeg 7
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Microsoft Research 8
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Center for Mathematics and Computer Science - Amsterdam 9
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HP Labs 9
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University of Cambridge 9
Xidian University 9
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University at Buffalo, State University of New York 9
Yahoo Research Labs 10
New York University Abu Dhabi 10
Universite de Toulouse 10
Dalian University of Technology 10
FX Palo Alto Laboratory 10
Korea Advanced Institute of Science & Technology 10
Federal University of Minas Gerais 10
Beijing University of Posts and Telecommunications 10
Swiss Federal Institute of Technology, Lausanne 11
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Ghent University 11
Portland State University 11
Technical University of Munich 12
University of Trento 12
University of Southern California 12
Aalto University 13
Shenzhen University 13
Curtin University of Technology, Perth 13
Motorola 13
Brunel University London 13
University of Mannheim 13
Beihang University 14
Carnegie Mellon University 14
University of Florence 15
Delft University of Technology 15
Technical University of Darmstadt 16
University of California, Berkeley 17
University of Texas at Dallas 17
Hefei University of Technology 17
University of Massachusetts Amherst 17
University of Central Florida 17
Academia Sinica Taiwan 18
Zhejiang University 18
City University of Hong Kong 18
Institute of Automation Chinese Academy of Sciences 18
Institute for Infocomm Research, A-Star, Singapore 19
National Taiwan University 19
Columbia University 20
Huazhong University of Science and Technology 21
Nanyang Technological University 21
University of Science and Technology of China 24
Sun Yat-Sen University 26
University of Illinois at Urbana-Champaign 26
Tsinghua University 27
University of Oslo 28
Hong Kong University of Science and Technology 28
Arizona State University 28
Microsoft Research Asia 32
Simon Fraser University 36
University of Ottawa, Canada 39
Chinese Academy of Sciences 42
National University of Singapore 132

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) - Special Section on Delay-Sensitive Video Computing in the Cloud and Special Section on Extended MMSys-NOSSDAV Best Papers
Archive


2018
Volume 14 Issue 3s, August 2018 Special Section on Delay-Sensitive Video Computing in the Cloud and Special Section on Extended MMSys-NOSSDAV Best Papers
Volume 14 Issue 3, August 2018  Issue-in-Progress
Volume 14 Issue 2s, May 2018
Volume 14 Issue 2, May 2018
Volume 14 Issue 1s, April 2018 Special Section on Representation, Analysis and Recognition of 3D Humans and Special Section on Multimedia Computing and Applications of Socio-Affective Behaviors in the Wild
Volume 14 Issue 1, January 2018

2017
Volume 13 Issue 4, October 2017
Volume 13 Issue 3s, August 2017 Special Section on Deep Learning for Mobile Multimedia and Special Section on Best Papers from ACM MMSys/NOSSDAV 2016
Volume 13 Issue 3, August 2017
Volume 13 Issue 2, May 2017
Volume 13 Issue 1, January 2017

2016
Volume 12 Issue 5s, December 2016 Special Section on Multimedia Big Data: Networking and Special Section on Best Papers From ACM MMSYS/NOSSDAV 2015
Volume 12 Issue 4s, November 2016 Special Section on Trust Management for Multimedia Big Data and Special Section on Best Papers of ACM Multimedia 2015
Volume 12 Issue 4, August 2016
Volume 12 Issue 3, June 2016
Volume 12 Issue 2, March 2016

2015
Volume 12 Issue 1s, October 2015 Special Issue on Smartphone-Based Interactive Technologies, Systems, and Applications and Special Issue on Extended Best Papers from ACM Multimedia 2014
Volume 12 Issue 1, August 2015
Volume 11 Issue 4, April 2015
Volume 11 Issue 2s, February 2015 Special Issue on MMSYS 2014
 
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