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Learning Object Interactions and Descriptions for Semantic Image Segmentation 30

Learning Object Interactions and Descriptions for Semantic I...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Guangrun Luo, Ping Lin, Liang Wang, Xiaogang Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Chinese Univ Hong Kong Shatin Hong Kong Peoples R China SenseTime Grp Ltd Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pat Rec Beijing Peoples R China

ISBN: (纸本)9781538604571

Recent advanced deep convolutional networks (CNNs) achieved great successes in many computer vision tasks, because of their compelling learning complexity and the presences of large-scale labeled data. However, as obtaining per-pixel annotations is expensive, performances of CNNs in semantic image segmentation are not fully exploited. this work significantly increases segmentation accuracy of CNNs by learning from an Image Descriptions in the Wild (IDW) dataset. Unlike previous image captioning datasets, where captions were manually and densely annotated, images and their descriptions in IDW are automatically downloaded from Internet without any manual cleaning and refinement. An IDW-CNN is proposed to jointly train IDW and existing image segmentation dataset such as Pascal VOC 2012 (VOC). It has two appealing properties. First, knowledge from different datasets can be fully explored and transferred from each other to improve performance. Second, segmentation accuracy in VOC can be constantly increased when selecting more data from IDW. Extensive experiments demonstrate the effectiveness and scalability of IDW-CNN, which outperforms existing best-performing system by 12% on VOC12 test set.

关键词： image segmentation captions Volatile Organic Compounds images Dataset Learning Objects Semantics computer vision Scalability

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Discover and Learn New Objects from Documentaries 30

Discover and Learn New Objects from Documentaries

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Kai Song, Hang Loy, Chen Change Lin, Dahua Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781538604571

Despite the remarkable progress in recent years, detecting objects in a new context remains a challenging task. Detectors learned from a public dataset can only work with a fixed list of categories, while training from scratch usually requires a large amount of training data with detailed annotations. this work aims to explore a novel approach - learning object detectors from documentary films in a weakly supervised manner. this is inspired by the observation that documentaries often provide dedicated exposition of certain object categories, where visual presentations are aligned with subtitles. We believe that object detectors can be learned from such a rich source of information. Towards this goal, we develop a joint probabilistic framework, where individual pieces of information, including video frames and subtitles, are brought together via both visual and linguistic links. On top of this formulation, we further derive a weakly supervised learning algorithm, where object model learning and training set mining are unified in an optimization procedure. Experimental results on a real world dataset demonstrate that this is an effective approach to learning new object detectors.

关键词： Detectors Visualization Pragmatics Training Proposals Optimization

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Position Determines Perspective: Investigating Perspective Distortion for Image Forensics of Faces 30

Position Determines Perspective: Investigating Perspective D...

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30th IEEE/CVF conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Peng, Bo Wang, Wei Dong, Jing Tan, Tieniu Chinese Acad Sci Inst Automat Ctr Res Intelligent Percept & Comp Beijing Peoples R China State Key Lab Cryptol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China State Key Lab Informat Secur Beijing Peoples R China

ISBN: (纸本)9781538607336

this paper points out a new telltale trace - the characteristic of perspective distortion (CPD), for the image forensics of faces. the perspective distortion is determined by the position of image shooting, and it is often overlooked when creating a forgery, which results in the inconsistency between the claimed camera parameters and the CPD in the face image. To investigate this consistency problem, we cast it to the consistency between the claimed camera intrinsic parameters and the estimated ones from the CPD. Our parameter estimation approach is based on geometric observations that are related to CPD, like facial landmarks and contours. We analyze the estimation uncertainty caused by indeterminacy of observation to obtain a more reliable forensic decision. Experiments on synthetic datasets and real forgery examples demonstrate the effectiveness of the proposed method.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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A Graph Regularized Deep Neural Network for Unsupervised Image Representation Learning 30

A Graph Regularized Deep Neural Network for Unsupervised Ima...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Shijie Li, Liang Wang, Shuhui Zhang, Weigang Huang, Qingming Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Peoples R China

ISBN: (纸本)9781538604571

Deep Auto-Encoder (DAE) has shown its promising power in high-level representation learning. From the perspective of manifold learning, we propose a graph regularized deep neural network (GR-DNN) to endue traditional DAEs with the ability of retaining local geometric structure. A deep-structured regularizer is formulated upon multi-layer perceptions to capture this structure. the robust and discriminative embedding space is learned to simultaneously preserve the high-level semantics and the geometric structure within local manifold tangent space. theoretical analysis presents the close relationship between the proposed graph regularizer and the graph Laplacian regularizer in terms of the optimization objective. We also alleviate the growth of the network complexity by introducing the anchor-based bipartite graph, which guarantees the good scalability for large scale data. the experiments on four datasets show the comparable results of the proposed GR-DNN with the state-of-the-art methods.

关键词： geometrical construction Line graph image representation Bipartite graph Neural network Manifolds Perspective

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Robust FEC-CNN: A High Accuracy Facial Landmark Detection System 30

Robust FEC-CNN: A High Accuracy Facial Landmark Detection Sy...

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30th IEEE/CVF conference on computer vision and pattern recognition Workshops (CVPRW)

作者： He, Zhenliang Zhang, Jie Kan, Meina Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China

ISBN: (纸本)9781538607336

Facial landmark detection, as a typical and crucial task in computer vision, is widely used in face recognition, face animation, facial expression analysis, etc. In the past decades, many efforts are devoted to designing robust facial landmark detection algorithms. However, it remains a challenging task due to extreme poses, exaggerated facial expression, unconstrained illumination, etc. In this work, we propose an effective facial landmark detection system, recorded as Robust FEC-CNN (RFC), which achieves impressive results on facial landmark detection in the wild. Considering the favorable ability of deep convolutional neural network, we resort to FEC-CNN as a basic method to characterize the complex nonlinearity from face appearance to shape. Moreover, face bounding box invariant technique is adopted to reduce the landmark localization sensitivity to the face detector while model ensemble strategy is adopted to further enhance the landmark localization performance. We participate the Menpo Facial Landmark Localisation in-the-Wild Challenge and our RFC significantly outperforms the baseline approach APS. Extensive experiments on Menpo Challenge dataset and IBUG dataset demonstrate the superior performance of the proposed RFC.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Discriminative Covariance Oriented Representation Learning for Face recognition with Image Sets 30

Discriminative Covariance Oriented Representation Learning f...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Wen Wang, Ruiping Shan, Shiguang Chen, Xilin Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Cooperat Medianet Innovat Ctr Beijing Peoples R China

ISBN: (纸本)9781538604571

For face recognition with image sets, while most existing works mainly focus on building robust set models with hand-crafted feature, it remains a research gap to learn better image representations which can closely match the subsequent image set modeling and classification. Taking sample covariance matrix as set model in the light of its recent promising success, we present a Discriminative Covariance oriented Representation Learning (DCRL) framework to bridge the above gap. the framework constructs a feature learning network (e.g. a CNN) to project the face images into a target representation space, and the network is trained towards the goal that the set covariance matrix calculated in the target space has maximum discriminative ability. To encode the discriminative ability of set covariance matrices, we elaborately design two different loss functions, which respectively lead to two different representation learning schemes, i.e., the Graph Embedding scheme and the Softmax Regression scheme. Both schemes optimize the whole network containing both image representation mapping and set model classification in a joint learning manner. the proposed method is extensively validated on three challenging and large scale databases for the task of face recognition with image sets, i.e., YouTube Celebrities, YouTube Face DB and Point-and-Shoot Challenge.

关键词： Facial recognition images Image image representation variance covariance matrix modeling light Imagery (Psychotherapy) image set LOSS FUNCTION Covariance YouTube SETTING Scheme Set (Psychology) Covariance matrix

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UntrimmedNets for Weakly Supervised Action recognition and Detection 30

UntrimmedNets for Weakly Supervised Action Recognition and D...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Limin Xiong, Yuanjun Lin, Dahua Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781538604571

Current action recognition methods heavily rely on trimmed videos for model training. However, it is expensive and time-consuming to acquire a large-scale trimmed video dataset. this paper presents a new weakly supervised architecture, called UntrimmedNet, which is able to directly learn action recognition models from untrimmed videos without the requirement of temporal annotations of action instances. Our UntrimmedNet couples two important components, the classification module and the selection module, to learn the action models and reason about the temporal duration of action instances, respectively. these two components are implemented with feed-forward networks, and UntrimmedNet is therefore an end-to-end trainable architecture. We exploit the learned models for action recognition (WSR) and detection (WSD) on the untrimmed video datasets of thUMOS14 and ActivityNet. Although our UntrimmedNet only employs weak supervision, our method achieves performance superior or comparable to that of those strongly supervised approaches on these two datasets.

关键词： Videos Proposals Feature extraction Training Motion pictures Adaptation models Visualization Videotapes Cinema Feature extraction Visualization Functional training Films Adaptation models sort module recognition (Psychology) Training Dataset Action

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Deep Crisp Boundaries 30

Deep Crisp Boundaries

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Yupei Zhao, Xin Huang, Kaiqi CASIA CRIPAC Beijing Peoples R China CASIA NLPR Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Tech Beijing Peoples R China

ISBN: (纸本)9781538604571

Edge detection has made significant progress with the help of deep Convolutional Networks (ConvNet). ConvNet based edge detectors approached human level performance on standard benchmarks. We provide a systematical study of these detector outputs, and show that they failed to accurately localize edges, which can be adversarial for tasks that require crisp edge inputs. In addition, we propose a novel refinement architecture to address the challenging problem of learning a crisp edge detector using ConvNet. Our method leverages a top-down backward refinement pathway, and progressively increases the resolution of feature maps to generate crisp edges. Our results achieve promising performance on BSDS500, surpassing human accuracy when using standard criteria, and largely outperforming state-of-the-art methods when using more strict criteria. We further demonstrate the benefit of crisp edge maps for estimating optical flow and generating object proposals.

关键词： Edge detection edges feature map Boundary value problems Grain boundaries boundary layer Refining Religious Missions Edge of a figure or solid Leverage Optic Flow Resolving power standardized benchmark

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Video Desnowing and Deraining Based on Matrix Decomposition 30

Video Desnowing and Deraining Based on Matrix Decomposition

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ren, Weihong Tian, Jiandong Han, Zhi Chan, Antoni Tang, Yandong Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China City Univ Hong Kong Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781538604571

the existing snow/rain removal methods often fail for heavy snow/rain and dynamic scene. One reason for the failure is due to the assumption that all the snowflakes/rain streaks are sparse in snow/rain scenes. the other is that the existing methods often can not differentiate moving objects and snowflakes/rain streaks. In this paper, we propose a model based on matrix decomposition for video desnowing and deraining to solve the problems mentioned above. We divide snowflakes/rain streaks into two categories: sparse ones and dense ones. With background fluctuations and optical flow information, the detection of moving objects and sparse snowflakes/rain streaks is formulated as a multi-label Markov Random Fields (MRFs). As for dense snowflakes/rain streaks, they are considered to obey Gaussian distribution. the snowflakes/rain streaks, including sparse ones and dense ones, in scene backgrounds are removed by low-rank representation of the backgrounds. Meanwhile, a group sparsity term in our model is designed to filter snow/rain pixels within the moving objects. Experimental results show that our proposed model performs better than the state-of-the-art methods for snow and rain removal.

关键词： Streak matrix decomposition Gaussian distribution Videotapes Moving Dense

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Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks 30

Modeling Temporal Dynamics and Spatial Configurations of Act...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Hongsong Wang, Liang Chinese Acad Sci CASIA NLPR CRIPAC Beijing Peoples R China Chinese Acad Sci Inst Automat CEBSIT Beijing Peoples R China UCAS Beijing Peoples R China

ISBN: (纸本)9781538604571

Recently, skeleton based action recognition gains more popularity due to cost-effective depth sensors coupled with real-time skeleton estimation algorithms. Traditional approaches based on handcrafted features are limited to represent the complexity of motion patterns. Recent methods that use Recurrent Neural Networks (RNN) to handle raw skeletons only focus on the contextual dependency in the temporal domain and neglect the spatial configurations of articulated skeletons. In this paper, we propose a novel two-stream RNN architecture to model both temporal dynamics and spatial configurations for skeleton based action recognition. We explore two different structures for the temporal stream: stacked RNN and hierarchical RNN. Hierarchical RNN is designed according to human body kinematics. We also propose two effective methods to model the spatial structure by converting the spatial graph into a sequence of joints. To improve generalization of our model, we further exploit 3D transformation based data augmentation techniques including rotation and scaling transformation to transform the 3D coordinates of skeletons during training. Experiments on 3D action recognition benchmark datasets show that our method brings a considerable improvement for a variety of actions, i.e., generic actions, interaction activities and gestures.

关键词： recurrent neural nets Skeletons bone structure Action Gestures

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