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"abstract": "To prevent moving cast shadows from being misunderstood as part of moving objects in change detection based video segmentation, this paper proposes a novel approach to cast shadow detection based on the edge and region information in multiple frames. First, an initial change detection mask containing moving objects and cast shadows is obtained. Then, a Canny edge map is generated. After that, the shadow region is detected and removed through multi-frame integration, edge matching and region growing. Finally, a post-processing procedure is used to eliminate noise and tune the boundaries of the objects. Our approach can be used for removing moving cast shadows in indoor environments for better video segmentation. The experimental results demonstrate the good performance of our algorithm.",
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"abstract": "Shadows are common aspect of images and when left undetected can hinder scene understanding and visual processing. We propose a simple yet effective approach based on reflectance to detect shadows from single image. An image is first segmented and based on the reflectance, illumination and texture characteristics, segments pairs are identified as shadow and non-shadow pairs. The proposed method is tested on two publicly available and widely used datasets. Our method achieves higher accuracy in detecting shadows compared to previous reported methods despite requiring fewer parameters. We also show results of shadow-free images by relighting the pixels in the detected shadow regions.",
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"abstract": "This paper is concerned with an appraisal for the satisfaction in the children's clothing market that embodies five aspects of indexes-outer appearance, functions, pricing, durability and comfortability by means of Fuzzy Comprehensive Evaluation Approach, Questionnaires as well as Interview Approach. This paper established two evaluation sets and constructed the fuzzy comprehensive evaluation model. Through the quantitative and qualitative analysis, the customers are quite satisfied with the overall children's clothing market, but they are not very satisfied with the price, the function, the durability and comfort of the children’s clothing, while they are quite satisfied with the appearance of the children’s clothing. Finally, the paper pointed out the main problems in current children's clothing market and provided advice on adjusting the operational strategies for some relevant enterprises.",
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"abstract": "A protective thermo-physiological clothing was developed, which provides intelligent thermal control function for a wearer by means of integration of smart materials, functional fabric, transducers, microelectronics, Artificial Intelligence (AI) control and wireless data acquisition. The clothing has a thermal control system, a wireless measurement system, a plurality of conductive fabrics, a semiconductor cooling system and the air circuit system. The clothing can provide automatic heating and cooling control according to wear conditions. The structure of the clothing, the technologies of intelligent control and wireless measurement, the experiments and the results of wearing trials and control tests are introduced.",
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"abstract": "In this paper we demonstrate an effective method for parsing clothing in fashion photographs, an extremely challenging problem due to the large number of possible garment items, variations in configuration, garment appearance, layering, and occlusion. In addition, we provide a large novel dataset and tools for labeling garment items, to enable future research on clothing estimation. Finally, we present intriguing initial results on using clothing estimates to improve pose identification, and demonstrate a prototype application for pose-independent visual garment retrieval.",
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"affiliation": "Stony Brook Univ., Stony Brook, NY, USA",
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"affiliation": "Stony Brook Univ., Stony Brook, NY, USA",
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"abstract": "Federated learning has attracted attention in recent years due to its native privacy-preserving features. However, it is still vulnerable to various membership inference attacks, such as backdoor, poisoning, and adversarial attacks. Membership Inference attack aims to discover the data used to train the model, which leads to privacy leaking ramifications on participants who use their local data to train the shared model. Recent research on countermeasure methods mainly focuses on protecting the parameters and has limitations in guaranteeing privacy while restraining the loss of the model. This paper proposes Fedefend, which applies adversarial examples to defend against membership inference attacks in federated learning. The proposed approach adds well-designed noise to the attack features of the target model of each iteration becomes an adversarial example. In addition, we also consider the utility loss of the model and use an adversarial method to generate noise to constrain the loss to a certain extent, which efficiently achieves a trade-off between privacy security and loss of the federated learning model. We evaluate the proposed Fedefend on two benchmark datasets, and the experimental results demonstrate that Fedefend has a good performance.",
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"abstract": "In recent years, the research community has increasingly focused on understanding the security and privacy challenges posed by deep learning models. However, the security domain and the privacy domain have typically been considered separately. It is thus unclear whether the defense methods in one domain will have any unexpected impact on the other domain. In this paper, we take a step towards enhancing our understanding of deep learning models when the two domains are combined together. We do this by measuring the success of membership inference attacks against two state-of-the-art adversarial defense methods that mitigate evasion attacks: adversarial training and provable defense. On the one hand, membership inference attacks aim to infer an individual's participation in the target model's training dataset and are known to be correlated with target model's overfitting. On the other hand, adversarial defense methods aim to enhance the robustness of target models by ensuring that model predictions are unchanged for a small area around each sample in the training dataset. Intuitively, adversarial defenses may rely more on the training dataset and be more vulnerable to membership inference attacks. By performing empirical membership inference attacks on both adversarially robust models and corresponding undefended models, we find that the adversarial training method is indeed more susceptible to membership inference attacks, and the privacy leakage is directly correlated with model robustness. We also find that the provable defense approach does not lead to enhanced success of membership inference attacks. However, this is achieved by significantly sacrificing the accuracy of the model on benign data points, indicating that privacy, security, and prediction accuracy are not jointly achieved in these two approaches.",
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"content": "In recent years, the research community has increasingly focused on understanding the security and privacy challenges posed by deep learning models. However, the security domain and the privacy domain have typically been considered separately. It is thus unclear whether the defense methods in one domain will have any unexpected impact on the other domain. In this paper, we take a step towards enhancing our understanding of deep learning models when the two domains are combined together. We do this by measuring the success of membership inference attacks against two state-of-the-art adversarial defense methods that mitigate evasion attacks: adversarial training and provable defense. On the one hand, membership inference attacks aim to infer an individual's participation in the target model's training dataset and are known to be correlated with target model's overfitting. On the other hand, adversarial defense methods aim to enhance the robustness of target models by ensuring that model predictions are unchanged for a small area around each sample in the training dataset. Intuitively, adversarial defenses may rely more on the training dataset and be more vulnerable to membership inference attacks. By performing empirical membership inference attacks on both adversarially robust models and corresponding undefended models, we find that the adversarial training method is indeed more susceptible to membership inference attacks, and the privacy leakage is directly correlated with model robustness. We also find that the provable defense approach does not lead to enhanced success of membership inference attacks. However, this is achieved by significantly sacrificing the accuracy of the model on benign data points, indicating that privacy, security, and prediction accuracy are not jointly achieved in these two approaches.",
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"affiliation": "National University of Singapore",
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"affiliation": "Princeton University",
"fullName": "Prateek Mittal",
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"abstract": "With an increasing popularity of Location-Based Services (LBSs), people's trajectories are continuously recorded and collected. The trajectory data are often shared or published for improving user experience, such as personalized recommendations and activity mining. However, releasing the trajectory data makes users' sensitive location visits vulnerable to inference attacks. In this paper, we study the problem of protecting sensitive location visits in the publication of trajectory data, assuming an adversary can do inference attacks using association rules derived from the data. We propose a methodology of anonymizing trajectories employing both generalizations and suppressions, to sanitize the trajectory data and protect sensitive location visits against inference attacks. We design a number of techniques to make our trajectory anonymizing algorithm efficient meanwhile maintaining the utility. We have conducted an empirical study to show that our algorithms can efficiently prevent inference attacks for real datasets while preserving the accuracy of aggregate querying on the published data.",
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"affiliation": "Shenyang Aerospace University",
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"title": "A Practical Defense against Attribute Inference Attacks in Session-based Recommendations",
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"abstract": "When users in various web and mobile applications enjoy the convenience of recommendation systems, they are vulnerable to attribute inference attacks. The accumulating online behaviors of users (e.g., clicks, searches, ratings) naturally brings out user preferences, and poses an inevitable threat of privacy that adversaries can infer one's private profiles (e.g., gender, sexual orientation, political view) with AI-based algorithms. Existing defense methods assume the existence of a trusted third party, rely on computationally intractable algorithms, or have impact on recommendation utility. These imperfections make them impractical for privacy preservation in real-life scenarios. In this work, we introduce BiasBooster, a practical proactive defense method based on behavior segmentation, to protect user privacy against attribute inference attacks from user behaviors, while retaining recommendation utility with a heuristic recommendation aggregation module. BiasBooster is a user-centric approach from client side, which proactively divides a user's behaviors into weakly related segments and perform them with several dummy identities, then aggregates real-time recommendations for user from different dummy identities. We estimate its effectiveness of preservation on both privacy and recommendation utility through extensive evaluations on two real-world datasets. A Chrome extension is conducted to demonstrate the feasibility of applying BiasBooster in real world. Experimental results show that compared to existing defenses, BiasBooster substantially reduces the averaged accuracy of attribute inference attacks, with minor utility loss of recommendations.",
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{
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"content": "When users in various web and mobile applications enjoy the convenience of recommendation systems, they are vulnerable to attribute inference attacks. The accumulating online behaviors of users (e.g., clicks, searches, ratings) naturally brings out user preferences, and poses an inevitable threat of privacy that adversaries can infer one's private profiles (e.g., gender, sexual orientation, political view) with AI-based algorithms. Existing defense methods assume the existence of a trusted third party, rely on computationally intractable algorithms, or have impact on recommendation utility. These imperfections make them impractical for privacy preservation in real-life scenarios. In this work, we introduce BiasBooster, a practical proactive defense method based on behavior segmentation, to protect user privacy against attribute inference attacks from user behaviors, while retaining recommendation utility with a heuristic recommendation aggregation module. BiasBooster is a user-centric approach from client side, which proactively divides a user's behaviors into weakly related segments and perform them with several dummy identities, then aggregates real-time recommendations for user from different dummy identities. We estimate its effectiveness of preservation on both privacy and recommendation utility through extensive evaluations on two real-world datasets. A Chrome extension is conducted to demonstrate the feasibility of applying BiasBooster in real world. Experimental results show that compared to existing defenses, BiasBooster substantially reduces the averaged accuracy of attribute inference attacks, with minor utility loss of recommendations.",
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"normalizedAbstract": "When users in various web and mobile applications enjoy the convenience of recommendation systems, they are vulnerable to attribute inference attacks. The accumulating online behaviors of users (e.g., clicks, searches, ratings) naturally brings out user preferences, and poses an inevitable threat of privacy that adversaries can infer one's private profiles (e.g., gender, sexual orientation, political view) with AI-based algorithms. Existing defense methods assume the existence of a trusted third party, rely on computationally intractable algorithms, or have impact on recommendation utility. These imperfections make them impractical for privacy preservation in real-life scenarios. In this work, we introduce BiasBooster, a practical proactive defense method based on behavior segmentation, to protect user privacy against attribute inference attacks from user behaviors, while retaining recommendation utility with a heuristic recommendation aggregation module. BiasBooster is a user-centric approach from client side, which proactively divides a user's behaviors into weakly related segments and perform them with several dummy identities, then aggregates real-time recommendations for user from different dummy identities. We estimate its effectiveness of preservation on both privacy and recommendation utility through extensive evaluations on two real-world datasets. A Chrome extension is conducted to demonstrate the feasibility of applying BiasBooster in real world. Experimental results show that compared to existing defenses, BiasBooster substantially reduces the averaged accuracy of attribute inference attacks, with minor utility loss of recommendations.",
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"authors": [
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"affiliation": "School of Cyber Security, University of Chinese Academy of Sciences,China",
"fullName": "Yifei Zhang",
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"affiliation": "SKLOIS, Institute of Information Engineering, CAS,China",
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"affiliation": "School of Cyber Security, University of Chinese Academy of Sciences,China",
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"abstract": "In order to preserve the EEG time-frequency domain features while fully uncovering the information flow and spatial information in the causal connectivity of relevant brain regions, this paper proposes a multichannel EEG signal emotion recognition method based on partial directed coherence dense graph propagation. The proposed method mainly uses partial directed coherence (PDC) to construct causal brain networks, which is used to describe the interactions and information flow relationships between different nodes or brain regions in the brain, and uses a modified model of dense graph propagation (DGP) in graph convolutional neural networks to train the graph data modeled by multichannel causal connectivity information of EEG signals and single-channel time-frequency domain features. Unlike traditional graph convolutional neural networks, the PDC-DGP model focuses on the directedness of the graph and does not dilute the feature information of the graph data by extensive smoothing. We investigate on two publicly available datasets, DEAP and SEED, and the results show that the method has good recognition performance on both datasets, and the average accurate recognition rate can be as high as 85.25% on SEED and 77.37% and 76.92% on the DEAP dataset for valence and arousal, respectively.",
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"abstract": "Visual dialog is a challenging vision-language task, which requires the agent to answer multi-round questions about an image. It typically needs to address two major problems: (1) How to answer visually-grounded questions, which is the core challenge in visual question answering (VQA); (2) How to infer the co-reference between questions and the dialog history. An example of visual co-reference is: pronouns (e.g., “they”) in the question (e.g., “Are they on or off?”) are linked with nouns (e.g., “lamps”) appearing in the dialog history (e.g., “How many lamps are there?”) and the object grounded in the image. In this work, to resolve the visual co-reference for visual dialog, we propose a novel attention mechanism called Recursive Visual Attention (RvA). Specifically, our dialog agent browses the dialog history until the agent has sufficient confidence in the visual co-reference resolution, and refines the visual attention recursively. The quantitative and qualitative experimental results on the large-scale VisDial v0.9 and v1.0 datasets demonstrate that the proposed RvA not only outperforms the state-of-the-art methods, but also achieves reasonable recursion and interpretable attention maps without additional annotations. The code is available at https://github.com/yuleiniu/rva.",
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"content": "Visual dialog is a challenging vision-language task, which requires the agent to answer multi-round questions about an image. It typically needs to address two major problems: (1) How to answer visually-grounded questions, which is the core challenge in visual question answering (VQA); (2) How to infer the co-reference between questions and the dialog history. An example of visual co-reference is: pronouns (e.g., “they”) in the question (e.g., “Are they on or off?”) are linked with nouns (e.g., “lamps”) appearing in the dialog history (e.g., “How many lamps are there?”) and the object grounded in the image. In this work, to resolve the visual co-reference for visual dialog, we propose a novel attention mechanism called Recursive Visual Attention (RvA). Specifically, our dialog agent browses the dialog history until the agent has sufficient confidence in the visual co-reference resolution, and refines the visual attention recursively. The quantitative and qualitative experimental results on the large-scale VisDial v0.9 and v1.0 datasets demonstrate that the proposed RvA not only outperforms the state-of-the-art methods, but also achieves reasonable recursion and interpretable attention maps without additional annotations. The code is available at https://github.com/yuleiniu/rva.",
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"normalizedAbstract": "Visual dialog is a challenging vision-language task, which requires the agent to answer multi-round questions about an image. It typically needs to address two major problems: (1) How to answer visually-grounded questions, which is the core challenge in visual question answering (VQA); (2) How to infer the co-reference between questions and the dialog history. An example of visual co-reference is: pronouns (e.g., “they”) in the question (e.g., “Are they on or off?”) are linked with nouns (e.g., “lamps”) appearing in the dialog history (e.g., “How many lamps are there?”) and the object grounded in the image. In this work, to resolve the visual co-reference for visual dialog, we propose a novel attention mechanism called Recursive Visual Attention (RvA). Specifically, our dialog agent browses the dialog history until the agent has sufficient confidence in the visual co-reference resolution, and refines the visual attention recursively. The quantitative and qualitative experimental results on the large-scale VisDial v0.9 and v1.0 datasets demonstrate that the proposed RvA not only outperforms the state-of-the-art methods, but also achieves reasonable recursion and interpretable attention maps without additional annotations. The code is available at https://github.com/yuleiniu/rva.",
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"Visual Question Answering",
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"Visual Dialog",
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"affiliation": "Renmin Univ. of China",
"fullName": "Yulei Niu",
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"surname": "Niu",
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{
"affiliation": "Nanyang Technological Univ.",
"fullName": "Hanwang Zhang",
"givenName": "Hanwang",
"surname": "Zhang",
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{
"affiliation": "Renmin Univ. of China",
"fullName": "Manli Zhang",
"givenName": "Manli",
"surname": "Zhang",
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{
"affiliation": "Renmin Univ. of China",
"fullName": "Jianhong Zhang",
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"surname": "Zhang",
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{
"affiliation": "Renmin Univ. of China",
"fullName": "Zhiwu Lu",
"givenName": "Zhiwu",
"surname": "Lu",
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{
"affiliation": "Renmin Univ. of China",
"fullName": "Ji-Rong Wen",
"givenName": "Ji-Rong",
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"abstract": "This paper conducts a systematic study on the role of visual attention in Unsupervised Video Object Segmentation (UVOS) tasks. By elaborately annotating three popular video segmentation datasets (DAVIS, Youtube-Objects and SegTrack V2) with dynamic eye-tracking data in the UVOS setting, for the first time, we quantitatively verified the high consistency of visual attention behavior among human observers, and found strong correlation between human attention and explicit primary object judgements during dynamic, task-driven viewing. Such novel observations provide an in-depth insight into the underlying rationale behind UVOS. Inspired by these findings, we decouple UVOS into two sub-tasks: UVOS-driven Dynamic Visual Attention Prediction (DVAP) in spatiotemporal domain, and Attention-Guided Object Segmentation (AGOS) in spatial domain. Our UVOS solution enjoys three major merits: 1) modular training without using expensive video segmentation annotations, instead, using more affordable dynamic fixation data to train the initial video attention module and using existing fixation-segmentation paired static/image data to train the subsequent segmentation module; 2) comprehensive foreground understanding through multi-source learning; and 3) additional interpretability from the biologically-inspired and assessable attention. Experiments on popular benchmarks show that, even without using expensive video object mask annotations, our model achieves compelling performance in comparison with state-of-the-arts.",
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"content": "This paper conducts a systematic study on the role of visual attention in Unsupervised Video Object Segmentation (UVOS) tasks. By elaborately annotating three popular video segmentation datasets (DAVIS, Youtube-Objects and SegTrack V2) with dynamic eye-tracking data in the UVOS setting, for the first time, we quantitatively verified the high consistency of visual attention behavior among human observers, and found strong correlation between human attention and explicit primary object judgements during dynamic, task-driven viewing. Such novel observations provide an in-depth insight into the underlying rationale behind UVOS. Inspired by these findings, we decouple UVOS into two sub-tasks: UVOS-driven Dynamic Visual Attention Prediction (DVAP) in spatiotemporal domain, and Attention-Guided Object Segmentation (AGOS) in spatial domain. Our UVOS solution enjoys three major merits: 1) modular training without using expensive video segmentation annotations, instead, using more affordable dynamic fixation data to train the initial video attention module and using existing fixation-segmentation paired static/image data to train the subsequent segmentation module; 2) comprehensive foreground understanding through multi-source learning; and 3) additional interpretability from the biologically-inspired and assessable attention. Experiments on popular benchmarks show that, even without using expensive video object mask annotations, our model achieves compelling performance in comparison with state-of-the-arts.",
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"affiliation": "Beijing Institute of Technology",
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"abstract": "Several recent studies have demonstrated the promise of deep visuomotor policies for robot manipulator control. Despite impressive progress, these systems are known to be vulnerable to physical disturbances, such as accidental or adversarial bumps that make them drop the manipulated object. They also tend to be distracted by visual disturbances such as objects moving in the robot's field of view, even if the disturbance does not physically prevent the execution of the task. In this paper, we propose an approach for augmenting a deep visuomotor policy trained through demonstrations with Task Focused visual Attention (TFA). The manipulation task is specified with a natural language text such as \"move the red bowl to the left\". This allows the visual attention component to concentrate on the current object that the robot needs to manipulate. We show that even in benign environments, the TFA allows the policy to consistently outperform a variant with no attention mechanism. More importantly, the new policy is significantly more robust: it regularly recovers from severe physical disturbances (such as bumps causing it to drop the object) from which the baseline policy, i.e. with no visual attention, almost never recovers. In addition, we show that the proposed policy performs correctly in the presence of a wide class of visual disturbances, exhibiting a behavior reminiscent of human selective visual attention experiments.",
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"content": "Several recent studies have demonstrated the promise of deep visuomotor policies for robot manipulator control. Despite impressive progress, these systems are known to be vulnerable to physical disturbances, such as accidental or adversarial bumps that make them drop the manipulated object. They also tend to be distracted by visual disturbances such as objects moving in the robot's field of view, even if the disturbance does not physically prevent the execution of the task. In this paper, we propose an approach for augmenting a deep visuomotor policy trained through demonstrations with Task Focused visual Attention (TFA). The manipulation task is specified with a natural language text such as \"move the red bowl to the left\". This allows the visual attention component to concentrate on the current object that the robot needs to manipulate. We show that even in benign environments, the TFA allows the policy to consistently outperform a variant with no attention mechanism. More importantly, the new policy is significantly more robust: it regularly recovers from severe physical disturbances (such as bumps causing it to drop the object) from which the baseline policy, i.e. with no visual attention, almost never recovers. In addition, we show that the proposed policy performs correctly in the presence of a wide class of visual disturbances, exhibiting a behavior reminiscent of human selective visual attention experiments.",
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"title": "Topological Modeling of Disordered Cellular Structures",
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"abstract": "We model the structure of space-filling disordered cellular systems. These systems are cellular networks with minimum incidence numbers (D+1 edges incident on a vertex in D-dimension). In the literature such systems are known as froths since the soap froth is the archetype of these structures. We present a method where the structure of froths is analyzed as organized in concentric layers of cells around a given, arbitrary, central cell. A simple map gives, by recursion, the number of cells in each layer. The map has one parameter, given as a function of the average topological properties of the cells in the neighboring layers. From the behavior of the number of cells per layer with the topological distance, one obtains the curvature of the space tiled by the froth. By using the map it is therefore possible to characterize the shape of the manifold tiled by the froth in term of the topological arrangements of its tiles. In two dimensions, we propose a method to deduce the Gaussian curvature of surfaces from a set of sampled points. In three dimensions, we use the map to investigate the freedom in constructing disordered Euclidean cellular structures. Among the closed packed structures, we find the average shape of the cells that maximize this freedom in filling space.",
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"content": "We model the structure of space-filling disordered cellular systems. These systems are cellular networks with minimum incidence numbers (D+1 edges incident on a vertex in D-dimension). In the literature such systems are known as froths since the soap froth is the archetype of these structures. We present a method where the structure of froths is analyzed as organized in concentric layers of cells around a given, arbitrary, central cell. A simple map gives, by recursion, the number of cells in each layer. The map has one parameter, given as a function of the average topological properties of the cells in the neighboring layers. From the behavior of the number of cells per layer with the topological distance, one obtains the curvature of the space tiled by the froth. By using the map it is therefore possible to characterize the shape of the manifold tiled by the froth in term of the topological arrangements of its tiles. In two dimensions, we propose a method to deduce the Gaussian curvature of surfaces from a set of sampled points. In three dimensions, we use the map to investigate the freedom in constructing disordered Euclidean cellular structures. Among the closed packed structures, we find the average shape of the cells that maximize this freedom in filling space.",
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"title": "“RegionCut” — Interactive multi-label segmentation utilizing cellular automaton",
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"abstract": "This paper addresses the problem of interactive image segmentation. We propose an extension of the GrowCut framework which follows Cellular Automaton theory and is comparable to a label propagation algorithm. Therefore, user labels are propagated according to Cellular Automaton until convergency. A common problem of GrowCut is the time consuming user initialization which requires distributed seeds. Our main contribution focuses on determining such an initialization utilizing GMMs and spherical coordinates. Furthermore we propose a new weight function based on the mean image gradient. According to our evaluation, our extensions result in a simplified user interaction and in better results in terms of accuracy and running time. Our experiments show that our method can compete with state-of-the-art energy minimization frameworks.",
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"abstract": "Graphic designers and other visual problem solving experts now routinely use computer-based image-editing tools in their work. Recently, attempts have been made to apply learning and inference techniques from artificial intelligence techniques to graphical editors in order to provide intelligent assistance to design professionals. The success of these attempts will depend on whether the programs can successfully capture the design knowledge of their users. But what is the nature of this knowledge? Because AI techniques have usually been applied in such areas as medicine or engineering rather than visual design, little is known about how design knowledge might differ from knowledge in other fields. This paper reports the results of an informal knowledge engineering study to try to understand how knowledge is communicated between humans in graphic design. Nowhere is the process of design communication more critical than in teaching beginning designers, since the effectiveness of the communication is crucial to the success of the student. The study examined books intended to teach graphic design to novices, and tried to analyze the nature of the communication with a view toward applying the results to a knowledge acquisition system for graphic design applications.",
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"abstract": "Multi-action video recognition is much more challenging due to the requirement to recognize multiple actions co-occurring simultaneously or sequentially. Modeling multi-action relations is beneficial and crucial to understand videos with multiple actions, and actions in a video are usually presented in multiple modalities. In this paper, we propose a novel multi-action relation model for videos, by leveraging both relational graph convolutional networks (GCNs) and video multi-modality. We first build multi-modal GCNs to explore modality-aware multi-action relations, fed by modality-specific action representation as node features, i.e., spatiotemporal features learned by 3D convolutional neural network (CNN), audio and textual embeddings queried from respective feature lexicons. We then joint both multi-modal CNN-GCN models and multi-modal feature representations for learning better relational action predictions. Ablation study, multi-action relation visualization, and boosts analysis, all show efficacy of our multi-modal multi-action relation modeling. Also our method achieves state-of-the-art performance on large-scale multi-action M-MiT benchmark. Our code is made publicly available at https://github.com/zhenglab/multi-action-video.",
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"content": "Multi-action video recognition is much more challenging due to the requirement to recognize multiple actions co-occurring simultaneously or sequentially. Modeling multi-action relations is beneficial and crucial to understand videos with multiple actions, and actions in a video are usually presented in multiple modalities. In this paper, we propose a novel multi-action relation model for videos, by leveraging both relational graph convolutional networks (GCNs) and video multi-modality. We first build multi-modal GCNs to explore modality-aware multi-action relations, fed by modality-specific action representation as node features, i.e., spatiotemporal features learned by 3D convolutional neural network (CNN), audio and textual embeddings queried from respective feature lexicons. We then joint both multi-modal CNN-GCN models and multi-modal feature representations for learning better relational action predictions. Ablation study, multi-action relation visualization, and boosts analysis, all show efficacy of our multi-modal multi-action relation modeling. Also our method achieves state-of-the-art performance on large-scale multi-action M-MiT benchmark. Our code is made publicly available at https://github.com/zhenglab/multi-action-video.",
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"abstract": "Recent advances in 3D human shape estimation build upon parametric representations that model very well the shape of the naked body, but are not appropriate to represent the clothing geometry. In this paper, we present an approach to model dressed humans and predict their geometry from single images. We contribute in three fundamental aspects of the problem, namely, a new dataset, a novel shape parameterization algorithm and an end-to-end deep generative network for predicting shape. First, we present 3D-People, a large-scale synthetic dataset with 2 Million photo-realistic images of 80 subjects performing 70 activities and wearing diverse outfits. Besides providing textured 3D meshes for clothes and body we annotated the dataset with segmentation masks, skeletons, depth, normal maps and optical flow. All this together makes 3D-People suitable for a plethora of tasks. We then represent the 3D shapes using 2D geometry images. To build these images we propose a novel spherical area-preserving parameterization algorithm based on the optimal mass transportation method. We show this approach to improve existing spherical maps which tend to shrink the elongated parts of the full body models such as the arms and legs, making the geometry images incomplete. Finally, we design a multi-resolution deep generative network that, given an input image of a dressed human, predicts his/her geometry image (and thus the clothed body shape) in an end-to-end manner. We obtain very promising results in jointly capturing body pose and clothing shape, both for synthetic validation and on the wild images.",
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"content": "Recent advances in 3D human shape estimation build upon parametric representations that model very well the shape of the naked body, but are not appropriate to represent the clothing geometry. In this paper, we present an approach to model dressed humans and predict their geometry from single images. We contribute in three fundamental aspects of the problem, namely, a new dataset, a novel shape parameterization algorithm and an end-to-end deep generative network for predicting shape. First, we present 3D-People, a large-scale synthetic dataset with 2 Million photo-realistic images of 80 subjects performing 70 activities and wearing diverse outfits. Besides providing textured 3D meshes for clothes and body we annotated the dataset with segmentation masks, skeletons, depth, normal maps and optical flow. All this together makes 3D-People suitable for a plethora of tasks. We then represent the 3D shapes using 2D geometry images. To build these images we propose a novel spherical area-preserving parameterization algorithm based on the optimal mass transportation method. We show this approach to improve existing spherical maps which tend to shrink the elongated parts of the full body models such as the arms and legs, making the geometry images incomplete. Finally, we design a multi-resolution deep generative network that, given an input image of a dressed human, predicts his/her geometry image (and thus the clothed body shape) in an end-to-end manner. We obtain very promising results in jointly capturing body pose and clothing shape, both for synthetic validation and on the wild images.",
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"keywords": [
"Clothing",
"Computational Geometry",
"Image Representation",
"Image Resolution",
"Image Segmentation",
"Image Sequences",
"Mesh Generation",
"Neural Nets",
"Realistic Images",
"Shape Recognition",
"Geometry Image",
"Clothed Body Shape",
"Clothing Shape",
"3 D People",
"3 D Human Shape Estimation",
"Parametric Representations",
"Clothing Geometry",
"Shape Parameterization Algorithm",
"End To End Deep Generative Network",
"3 D People",
"Large Scale Synthetic Dataset",
"Spherical Area Preserving Parameterization Algorithm",
"Body Models",
"Multiresolution Deep Generative Network",
"Body Models",
"Textured 3 D Meshes",
"Segmentation Masks",
"Optical Flow",
"Optimal Mass Transportation Method",
"Shape",
"Geometry",
"Three Dimensional Displays",
"Clothing",
"Cameras",
"Two Dimensional Displays",
"Optical Imaging"
],
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"fullName": "Jordi Sanchez",
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"affiliation": "Harvard University",
"fullName": "Gary P. T. Choi",
"givenName": "Gary P. T.",
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"fullName": "Alberto Sanfeliu",
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"fullName": "Francesc Moreno",
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