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Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detection

Progressive Attention on Multi-Level Dense Difference Maps f...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Tang, Jiaqi Liu, Zhaoyang Qian, Chen Wu, Wayne Wang, Limin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China SenseTime Res Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Generic event boundary detection (GEBD) is an important yet challenging task in video understanding, which aims at detecting the moments where humans naturally perceive event boundaries. The main challenge of this task is perceiving various temporal variations of diverse event boundaries. To this end, this paper presents an effective and end-to-end learnable framework (DDM-Net). To tackle the diversity and complicated semantics of event boundaries, we make three notable improvements. First, we construct a feature bank to store multi-level features of space and time, prepared for difference calculation at multiple scales. Second, to alleviate inadequate temporal modeling of previous methods, we present dense difference maps (DDM) to comprehensively characterize the motion pattern. Finally, we exploit progressive attention on multi-level DDM to jointly aggregate appearance and motion clues. As a result, DDM-Net respectively achieves a significant boost of 14% and 8% on Kinetics-GEBD and TAPOS benchmark, and outperforms the top-I winner solution of LOVED Challenge @ CVPR 2021 without bells and whistles. The state-of-the-art result demonstrates the effectiveness of richer motion representation and more sophisticated aggregation, in handling the diversity of GEBD. The code is made available at https://***/MCG-NJU/DDM.

关键词： Representation learning computer vision Codes Aggregates Semantics Benchmark testing pattern recognition

来源：评论

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Semantic Component Decomposition for Face Attribute Manipulation 32

Semantic Component Decomposition for Face Attribute Manipula...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Ying-Cong Shen, Xiaohui Lin, Zhe Lu, Xin Pao, I-Ming Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Tencent Youtu Lab Shanghai Peoples R China Adobe Res San Jose CA USA ByteDance AI Lab Beijing Peoples R China

ISBN: (纸本)9781728132938

Deep neural network-based methods were proposed for face attribute manipulation. There still exist, however, two major issues, i.e., insufficient visual quality (or resolution) of the results and lack of user control. They limit the applicability of existing methods since users may have different editing preference on facial attributes. In this paper, we address these issues by proposing a semantic component model. The model decomposes a facial attribute into multiple semantic components, each corresponds to a specific face region. This not only allows for user control of edit strength on different parts based on their preference, but also makes it effective to remove unwanted edit effect. Further, each semantic component is composed of two fundamental elements, which determine the edit effect and region respectively. This property provides fine interactive control. As shown in experiments, our model not only produces high quality results, but also allows effective user interaction.

关键词： And Body Pose Face Gesture Image and Video Synthesis vision Applications and Systems

来源：评论

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Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection 32

Contrast Prior and Fluid Pyramid Integration for RGBD Salien...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Jia-Xing Cao, Yang Fan, Deng-Ping Cheng, Ming-Ming Li, Xuan-Yi Zhang, Le Nankai Univ CS TKLNDST Tianjin Peoples R China ASTAR Singapore Singapore

ISBN: (纸本)9781728132938

The large availability of depth sensors provides valuable complementary information for salient object detection (SOD) in RGBD images. However, due to the inherent difference between RGB and depth information, extracting features from the depth channel using ImageNet pre-trained backbone models and fusing them with RGB features directly are sub-optimal. In this paper, we utilize contrast prior;which used to be a dominant cue in none deep learning based SOD approaches, into CNNs-based architecture to enhance the depth information. The enhanced depth cues are further integrated with RGB features for SOD, using a novel fluid pyramid integration, which can make better use of multi-scale cross-modal features. Comprehensive experiments on 5 challenging benchmark datasets demonstrate the superiority of the architecture CPFP over 9 state-of-the-art alternative methods.

关键词： Low-level vision RGBD sensors and analytics

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Context-Based Trit-Plane Coding for Progressive Image Compression

Context-Based Trit-Plane Coding for Progressive Image Compre...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jeon, Seungmin Choi, Kwang Pyo Park, Youngo Kim, Chang-Su Korea Univ Seoul South Korea Samsung Elect Seoul South Korea

ISBN: (纸本)9798350301298

Trit-plane coding enables deep progressive image compression, but it cannot use autoregressive context models. In this paper, we propose the context-based trit-plane coding (CTC) algorithm to achieve progressive compression more compactly. First, we develop the context-based rate reduction module to estimate trit probabilities of latent elements accurately and thus encode the trit-planes compactly. Second, we develop the context-based distortion reduction module to refine partial latent tensors from the trit-planes and improve the reconstructed image quality. Third, we propose a retraining scheme for the decoder to attain better rate-distortion tradeoffs. Extensive experiments show that CTC outperforms the baseline trit-plane codec significantly, e.g. by -14.84% in BD-rate on the Kodak lossless dataset, while increasing the time complexity only marginally. The source codes are available at https://***/seungminjeon-github/CTC.

关键词： Low-level vision

来源：评论

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Weakly-Supervised Dual Clustering for Image Semantic Segmentation

Weakly-Supervised Dual Clustering for Image Semantic Segment...

引用

26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Liu, Yang Liu, Jing Li, Zechao Tang, Jinhui Lu, Hanqing Chinese Acad Sci Inst Automat NLPR Beijing 100190 Peoples R China Nanjing Univ Sci & Tech Sch Comp Sci Beijing 210044 Peoples R China

ISBN: (纸本)9780769549897

In this paper, we propose a novel Weakly-Supervised Dual Clustering (WSDC) approach for image semantic segmentation with image-level labels, i.e., collaboratively performing image segmentation and tag alignment with those regions. The proposed approach is motivated from the observation that superpixels belonging to an object class usually exist across multiple images and hence can be gathered via the idea of clustering. In WSDC, spectral clustering is adopted to cluster the superpixels obtained from a set of over-segmented images. At the same time, a linear transformation between features and labels as a kind of discriminative clustering is learned to select the discriminative features among different classes. The both clustering outputs should be consistent as much as possible. Besides, weakly-supervised constraints from image-level labels are imposed to restrict the labeling of superpixels. Finally, the non-convex and non-smooth objective function are efficiently optimized using an iterative CCCP procedure. Extensive experiments conducted on MSRC and LabelMe datasets demonstrate the encouraging performance of our method in comparison with some state-of-the-arts.

关键词： image segmentation pattern clustering

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You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions

You2Me: Inferring Body Pose in Egocentric Video via First an...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ng, Evonne Xiang, Donglai Joo, Hanbyul Grauman, Kristen Univ Calif Berkeley Berkeley CA 94720 USA UT Austin Austin TX 78712 USA Carnegie Mellon Univ Pittsburgh PA USA Facebook AI Res Menlo Pk CA USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

The body pose of a person wearing a camera is of great interest for applications in augmented reality, healthcare, and robotics, yet much of the person's body is out of view for a typical wearable camera. We propose a learning-based approach to estimate the camera wearer's 3D body pose from egocentric video sequences. Our key insight is to leverage interactions with another person-whose body pose we can directly observe-as a signal inherently linked to the body pose of the first-person subject. We show that since interactions between individuals often induce a well-ordered series of back-and-forth responses, it is possible to learn a temporal model of the interlinked poses even though one party is largely out of view. We demonstrate our idea on a variety of domains with dyadic interaction and show the substantial impact on egocentric body pose estimation, which improves the state of the art.

关键词： Cameras Three-dimensional displays Feature extraction Robot vision systems Pose estimation Visualization Predictive models

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Group Sampling for Scale Invariant Face Detection 32

Group Sampling for Scale Invariant Face Detection

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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ming, Xiang Wei, Fangyun Zhang, Ting Chen, Dong Wen, Fang Xi An Jiao Tong Univ Xian Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781728132938

Detectors based on deep learning tend to detect multi-scale faces on a single input image for efficiency. Recent works, such as FPN and SSD, generally use feature maps from multiple layers with different spatial resolutions to detect objects at different scales, e.g., high-resolution feature maps for small objects. However, we find that such multi-layer prediction is not necessary. Faces at all scales can be well detected with features from a single layer of the network. In this paper, we carefully examine the factors affecting face detection across a large range of scales, and conclude that the balance of training samples, including both positive and negative ones, at different scales is the key. We propose a group sampling method which divides the anchors into several groups according to the scale, and ensure that the number of samples for each group is the same during training. Our approach using only the last layer of FPN as features is able to advance the state-of-the-arts. Comprehensive analysis and extensive experiments have been conducted to show the effectiveness of the proposed method. Our approach, evaluated on face detection benchmarks including FDDB and WIDER FACE datasets, achieves stateof-the-art results without bells and whistles.

关键词： And Body Pose Categorization Deep Learning Face Gesture recognition: Detection Retrieval vision Applications and Systems

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GIF2Video: Color Dequantization and Temporal Interpolation of GIF images 32

GIF2Video: Color Dequantization and Temporal Interpolation o...

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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Yang Huang, Haibin Wang, Chuan He, Tong Wang, Jue Minh Hoai SUNY Stony Brook Stony Brook NY USA Megvii Res USA Hangzhou Peoples R China UCLA Los Angeles CA USA

ISBN: (纸本)9781728132938

Graphics Interchange Format (GIF) is a highly portable graphics format that is ubiquitous on the Internet. Despite their small sizes, GIF images often contain undesirable visual artifacts such as flat color regions, false contours, color shift, and dotted patterns. In this paper, we propose GIF2Video, the first learning-based method for enhancing the visual quality of GIFs in the wild. We focus on the challenging task of GIF restoration by recovering information lost in the three steps of GIF creation: frame sampling, color quantization, and color dithering. We first propose a novel CNN architecture for color dequantization. It is built upon a compositional architecture for multi-step color correction, with a comprehensive loss function designed to handle large quantization errors. We then adapt the SuperSlomo network for temporal interpolation of GIF frames. We introduce two large datasets, namely GIF-Faces and GIF-Moments, for both training and evaluation. Experimental results show that our method can significantly improve the visual quality of GIFs, and outperforms direct baseline and state-of-the-art approaches.

关键词： Image and Video Synthesis vision Applications and Systems

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Adversarial Semantic Alignment for Improved Image Captions 32

Adversarial Semantic Alignment for Improved Image Captions

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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Dognin, Pierre Melnyk, Igor Mroueh, Youssef Ross, Jerret Sercu, Tom IBM Res Yorktown Hts NY 10598 USA

ISBN: (纸本)9781728132938

In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions. We empirically focus on the viability of two training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST, even without accessing discriminator gradients directly. We also address the problem of automatic evaluation for captioning models and introduce a new semantic score, and show its correlation to human judgement. As an evaluation paradigm, we argue that an important criterion for a captioner is the ability to generalize to compositions of objects that do not usually co-occur together. To this end, we introduce a small captioned Out of Context (OOC) test set. The OOC set, combined with our semantic score, are the proposed new diagnosis tools for the captioning community. When evaluated on OOC and MS-COCO benchmarks, we show that SCST-based training has a strong performance in both semantic score and human evaluation, promising to be a valuable new approach for efficient discrete GAN training.

关键词： Deep Learning vision + Language

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BASNet: Boundary-Aware Salient Object Detection 32

BASNet: Boundary-Aware Salient Object Detection

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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Qin, Xuebin Zhang, Zichen Huang, Chenyang Gao, Chao Dehghan, Masood Jagersand, Martin Univ Alberta Edmonton AB Canada

ISBN: (纸本)9781728132938

Deep Convolutional Neural Networks have been adopted for salient object detection and achieved the state-of-the-art performance. Most of the previous works however focus on region accuracy but not on the boundary quality. In this paper,we propose a predict-refine architecture, BASNet, and a new hybrid loss for Boundary-Aware Salient object detection. Specifically, the architecture is composed of a densely supervised Encoder-Decoder network and a residual refinement module, which are respectively in charge of saliency prediction and saliency map refinement. The hybrid loss guides the network to learn the transformation between the input image and the ground truth in a three-level hierarchy -pixel-, patch- and map- level- by fusing Binary Cross Entropy (BCE), Structural SIMilarity (SSIM) and Intersection-over-Union (IoU) losses. Equipped with the hybrid loss, the proposed predict-refine architecture is able to effectively segment the salient object regions and accurately predict the fine structures with clear boundaries. Experimental results on six public datasets show that our method outperforms the state-of-the-art methods both in terms of regional and boundary evaluation measures. Our method runs at over 25 fps on a single GPU. The code is available at: https://***/NathanUA/BASNet.

关键词： Deep Learning Grouping and Shape Segmentation vision Applications and Systems

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