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

作者： Pang, Jiangmiao Qiu, Linlu Li, Xia Chen, Haofeng Li, Qi Darrell, Trevor Yu, Fisher Zhejiang Univ Hangzhou Zhejiang Peoples R China Georgia Inst Technol Atlanta GA 30332 USA Swiss Fed Inst Technol Zurich Switzerland Stanford Univ Stanford CA 94305 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781665445092

Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions on the images. In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of region proposals on a pair of images for contrastive learning. We can directly combine this similarity learning with existing detection methods to build Quasi-Dense Tracking (QDTrack) without turning to displacement regression or motion priors. We also find that the resulting distinctive feature space admits a simple nearest neighbor search at the inference time. Despite its simplicity, QDTrack outperforms all existing methods on MOT, BDD100K, Waymo, and TAO tracking benchmarks. It achieves 68.7 MOTA at 20.3 FPS on MOT17 without using external training data. Compared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets.

关键词： Training Image segmentation computer vision Codes Training data Detectors Nearest neighbor methods

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ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

ClassSR: A General Framework to Accelerate Super-Resolution ...

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

作者： Kong, Xiangtao Zhao, Hengyuan Qiao, Yu Dong, Chao Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synergy Syst Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen Peoples R China

ISBN: (纸本)9781665445092

We aim at accelerating super-resolution (SR) networks on large images (2K-8K). The large images are usually decomposed into small sub-images in practical usages. Based on this processing, we found that different image regions have different restoration difficulties and can be processed by networks with different capacities. Intuitively, smooth areas are easier to super-solve than complex textures. To utilize this property, we can adopt appropriate SR networks to process different sub-images after the decomposition. On this basis, we propose a new solution pipeline - ClassSR that combines classification and SR in a unified framework. In particular, it first uses a Class-Module to classify the sub-images into different classes according to restoration difficulties, then applies an SR-Module to perform SR for different classes. The Class-Module is a conventional classification network, while the SR-Module is a network container that consists of the to-be-accelerated SR network and its simplified versions. We further introduce a new classification method with two losses - Class-Loss and Average-Loss to produce the classification results. After joint training, a majority of sub-images will pass through smaller networks, thus the computational cost can be significantly reduced. Experiments show that our ClassSR can help most existing methods (e.g., FSRCNN, CARN, SRResNet, RCAN) save up to 50% FLOPs on DIV8K datasets. This general framework can also be applied in other low-level vision tasks.

关键词： Training computer vision Superresolution Pipelines Containers Image restoration pattern recognition

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Learning the Superpixel in a Non-iterative and Lifelong Manner

Learning the Superpixel in a Non-iterative and Lifelong Mann...

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

作者： Zhu, Lei She, Qi Zhang, Bin Lu, Yanye Lu, Zhilin Li, Duo Hu, Jie Peking Univ Hlth Sci Ctr Inst Med Technol Beijing Peoples R China Bytedance AI Lab Beijing Peoples R China Peking Univ Dept Biomed Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Peking Univ Inst Biomed Engn Shenzhen Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Superpixel is generated by automatically clustering pixels in an image into hundreds of compact partitions, which is widely used to perceive the object contours for its excellent contour adherence. Although some works use the Convolution Neural Network (CNN) to generate high-quality superpixel, we challenge the design principles of these networks, specifically for their dependence on manual labels and excess computation resources, which limits their flexibility compared with the traditional unsupervised segmentation methods. We target at redefining the CNN-based superpixel segmentation as a lifelong clustering task and propose an unsupervised CNN-based method called LNS-Net. The LNS-Net can learn superpixel in a non-iterative and lifelong manner without any manual labels. Specifically, a lightweight feature embedder is proposed for LNS-Net to efficiently generate the cluster-friendly features. With those features, seed nodes can be automatically assigned to cluster pixels in a non-iterative way. Additionally, our LNS-Net can adapt the sequentially lifelong learning by rescaling the gradient of weight based on both channel and spatial context to avoid overfitting. Experiments show that the proposed LNS-Net achieves significantly better performance on three benchmarks with nearly ten times lower complexity compared with other state-of-the-art methods.

关键词： Image segmentation Convolution Neural networks Manuals Benchmark testing pattern recognition Finite element analysis

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Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild

Learning to Recommend Frame for Interactive Video Object Seg...

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

作者： Yin, Zhaoyuan Zheng, Jia Luo, Weixin Qian, Shenhan Zhang, Hanling Gao, Shenghua Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Manycore KooLab Rancho Cordova CA USA Meituan Grp Beijing Peoples R China ShanghaiTech Univ Shanghai Peoples R China Hunan Univ Sch Design Changsha Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9781665445092

This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively. Then, based on the user annotations, a segmentation algorithm refines the masks. The previous interactive VOS paradigm selects the frame with some worst evaluation metric, and the ground truth is required for calculating the evaluation metric, which is impractical in the testing phase. In contrast, in this paper, we advocate that the frame with the worst evaluation metric may not be exactly the most valuable frame that leads to the most performance improvement across the video. Thus, we formulate the frame selection problem in the interactive VOS as a Markov Decision Process, where an agent is learned to recommend the frame under a deep reinforcement learning framework. The learned agent can automatically determine the most valuable frame, making the interactive setting more practical in the wild. Experimental results on the public datasets show the effectiveness of our learned agent without any changes to the underlying VOS algorithms.

关键词： Measurement computer vision Codes Annotations Object segmentation Reinforcement learning Markov processes

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Causal Hidden Markov Model for Time Series Disease Forecasting

Causal Hidden Markov Model for Time Series Disease Forecasti...

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

作者： Li, Jing Wu, Botong Sun, Xinwei Wang, Yizhou Peking Univ Dept Comp Sci Beijing Peoples R China Peking Univ Adv Inst Info Tech Beijing Peoples R China Peking Univ Ctr Frontiers Comp Studies Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

We propose a causal hidden Markov model to achieve robust prediction of irreversible disease at an early stage, which is safety-critical and vital for medical treatment in early stages. Specifically, we introduce the hidden variables which propagate to generate medical data at each time step. To avoid learning spurious correlation (e.g., confounding bias), we explicitly separate these hidden variables into three parts: a) the disease (clinical)-related part;b) the disease (non-clinical)-related part;c) others, with only a),b) causally related to the disease however c) may contain spurious correlations (with the disease) inherited from the data provided. With personal attributes and disease label respectively provided as side information and supervision, we prove that these disease-related hidden variables can be disentangled from others, implying the avoidance of spurious correlation for generalization to medical data from other (out-of-) distributions. Guaranteed by this result, we propose a sequential variational auto-encoder with a reformulated objective function. We apply our model to the early prediction of peripapillary atrophy and achieve promising results on out-of-distribution test data. Further, the ablation study empirically shows the effectiveness of each component in our method. And the visualization shows the accurate identification of lesion regions from others. (1)

关键词： Correlation Time series analysis Hidden Markov models Medical treatment Predictive models Linear programming pattern recognition

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MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization

MultiBodySync: Multi-Body Segmentation and Motion Estimation...

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

作者： Huang, Jiahui Wang, He Birdal, Tolga Sung, Minhyuk Arrigoni, Federica Hu, Shi-Min Guibas, Leonidas Tsinghua Univ Beijing Peoples R China Stanford Univ Stanford CA 94305 USA Korea Adv Inst Sci & Technol Daejeon South Korea Univ Trento Trento TN Italy

ISBN: (纸本)9781665445092

We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds. The two non-trivial challenges posed by this multi-scan multibody setting that we investigate are: (i) guaranteeing correspondence and segmentation consistency across multiple input point clouds capturing different spatial arrangements of bodies or body parts;and (ii) obtaining robust motion-based rigid body segmentation applicable to novel object categories. We propose an approach to address these issues that incorporates spectral synchronization into an iterative deep declarative network, so as to simultaneously recover consistent correspondences as well as motion segmentation. At the same time, by explicitly disentangling the correspondence and motion segmentation estimation modules, we achieve strong generalizability across different object categories. Our extensive evaluations demonstrate that our method is effective on various datasets ranging from rigid parts in articulated objects to individually moving objects in a 3D scene, be it single-view or full point clouds. Code at https: //***/huangjh-pub/multibody-sync.

关键词： computer vision Three-dimensional displays Motion segmentation Scalability Motion estimation Pipelines Dynamics

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Structured Scene Memory for vision-Language Navigation

Structured Scene Memory for Vision-Language Navigation

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

作者： Wang, Hanqing Wang, Wenguan Liang, Wei Xiong, Caiming Shen, Jianbing Beijing Inst Technol Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland Salesforce Res San Francisco CA USA Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

Recently, numerous algorithms have been developed to tackle the problem of vision-language navigation (VLN), i.e., entailing an agent to navigate 3D environments through following linguistic instructions. However, current VLN agents simply store their past experiences/observations as latent states in recurrent networks, failing to capture environment layouts and make long-term planning. To address these limitations, we propose a crucial architecture, called Structured Scene Memory (SSM). It is compartmentalized enough to accurately memorize the percepts during navigation. It also serves as a structured scene representation, which captures and disentangles visual and geometric cues in the environment. SSM has a collect-read controller that adaptively collects information for supporting current decision making and mimics iterative algorithms for long-range reasoning. As SSM provides a complete action space, i.e., all the navigable places on the map, a frontier-exploration based navigation decision making strategy is introduced to enable efficient and global planning. Experiment results on two VLN datasets (i.e., R2R and R4R) show that our method achieves state-of-the-art performance on several metrics.

关键词： Visualization Three-dimensional displays Recurrent neural networks Navigation Decision making Layout Memory architecture

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OVMR: Open-Vocabulary recognition with Multi-Modal References

OVMR: Open-Vocabulary Recognition with Multi-Modal Reference...

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conference on computer vision and pattern recognition (cvpr)

作者： Zehong Ma Shiliang Zhang Longhui Wei Qi Tian National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Huawei Inc.

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The challenge of open-vocabulary recognition lies in the model has no clue of new categories it is applied to. Existing works have proposed different methods to embed category cues into the model, e.g., through few-shot fine-tuning, providing category names or textual descriptions to vision-Language Models. Fine-tuning is time-consuming and degrades the generalization capability. Textual descriptions could be ambiguous and fail to depict visual details. This paper tackles open-vocabulary recognition from a different perspective by referring to multi-modal clues composed of textual descriptions and exemplar images. Our method, named OVMR, adopts two innovative components to pursue a more robust category cues embedding. A multi-modal classifier is first generated by dynamically complementing textual descriptions with image exemplars. A preference-based refinement module is hence applied to fuse uni-modal and multi-modal classifiers, with the aim to alleviate issues of low-quality exemplar images or textual descriptions. The proposed OVMR is a plug-and-play module, and works well with exemplar images randomly crawled from the Internet. Extensive experiments have demonstrated the promising performance of OVMR, e.g., it outperforms existing methods across various scenarios and setups. Codes are publicly available at https://***/Zehong-Ma/OVMR.

关键词： Visualization computer vision Image recognition Codes Fuses pattern recognition Internet

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LiBRe: A Practical Bayesian Approach to Adversarial Detection

LiBRe: A Practical Bayesian Approach to Adversarial Detectio...

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

作者： Deng, Zhijie Yang, Xiao Xu, Shizhen Su, Hang Zhu, Jun Tsinghua Univ Dept Comp Sci & Tech BNRist Ctr Inst AITsinghua Bosch Joint ML CtrTHBI Lab Beijing 100084 Peoples R China RealAI Beijing Peoples R China

ISBN: (纸本)9781665445092

Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples. Various adversarial defense strategies have been proposed to resolve this problem, but they typically demonstrate restricted practicability owing to unsurmountable compromise on universality, effectiveness, or efficiency. In this work, we propose a more practical approach, Lightweight Bayesian Refinement (LiBRe), in the spirit of leveraging Bayesian neural networks (BNNs) for adversarial detection. Empowered by the task and attack agnostic modeling under Bayes principle, LiBRe can endow a variety of pre-trained task-dependent DNNs with the ability of defending heterogeneous adversarial attacks at a low cost. We develop and integrate advanced learning techniques to make LiBRe appropriate for adversarial detection. Concretely, we build the few-layer deep ensemble variational and adopt the pre-training & fine-tuning workflow to boost the effectiveness and efficiency of LiBRe. We further provide a novel insight to realise adversarial detection-oriented uncertainty quantification without inefficiently crafting adversarial examples during training. Extensive empirical studies covering a wide range of scenarios verify the practicability of LiBRe. We also conduct thorough ablation studies to evidence the superiority of our modeling and learning strategies.(1)

关键词： Training Deep learning Costs Uncertainty Neural networks Bayes methods pattern recognition

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SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network

SelfSAGCN: Self-Supervised Semantic Alignment for Graph Conv...

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

作者： Yang, Xu Deng, Cheng Dang, Zhiyuan Wei, Kun Yan, Junchi Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Shanghai Jiao Tong Univ Dept CSE Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

Graph convolution networks (GCNs) are a powerful deep learning approach and have been successfully applied to representation learning on graphs in a variety of real-world applications. Despite their success, two fundamental weaknesses of GCNs limit their ability to represent graph-structured data: poor performance when labeled data are severely scarce and indistinguishable features when more layers are stacked. In this paper, we propose a simple yet effective Self-Supervised Semantic Alignment Graph Convolution Network (SelfSAGCN), which consists of two crux techniques: Identity Aggregation and Semantic Alignment, to overcome these weaknesses. The behind basic idea is the node features in the same class but learned from semantic and graph structural aspects respectively, are expected to be mapped nearby. Specifically, the Identity Aggregation is applied to extract semantic features from labeled nodes, the Semantic Alignment is utilized to align node features obtained from different aspects using the class central similarity. In this way, the over-smoothing phenomenon is alleviated, while the similarities between the unlabeled features and labeled ones from the same class are enhanced. Experimental results on five popular datasets show that the proposed SelfSAGCN outperforms state-of-the-art methods on various classification tasks.

关键词： Deep learning computer vision Convolution Computational modeling Semantics Benchmark testing Feature extraction

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