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Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification

Learning to Generalize Unseen Domains via Memory-based Multi...

引用

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

作者： Zhao, Yuyang Zhong, Zhun Yang, Fengxiang Luo, Zhiming Lin, Yaojin Li, Shaozi Sebe, Nicu Xiamen Univ Sch Informat Dept Artificial Intelligence Xiamen Fujian Peoples R China Univ Trento Dept Informat Engn & Comp Sci Trento Italy Xiamen Univ Inst Artificial Intelligence Xiamen Fujian Peoples R China Minnan Normal Univ Zhangzhou Fujian Peoples R China Xiamen Univ Xiamen Fujian Peoples R China

ISBN: (纸本)9781665445092

Recent advances in person re-identification (ReID) obtain impressive accuracy in the supervised and unsupervised learning settings. However, most of the existing methods need to train a new model for a new domain by accessing data. Due to public privacy, the new domain data are not always accessible, leading to a limited applicability of these methods. In this paper, we study the problem of multi-source domain generalization in ReID, which aims to learn a model that can perform well on unseen domains with only several labeled source domains. To address this problem, we propose the Memory-based Multi-Source Meta-Learning ((ML)-L-3) framework to train a generalizable model for unseen domains. Specifically, a meta-learning strategy is introduced to simulate the train-test process of domain generalization for learning more generalizable models. To overcome the unstable meta-optimization caused by the parametric classifier, we propose a memory-based identification loss that is non-parametric and harmonizes with meta-learning. We also present a meta batch normalization layer (MetaBN) to diversify meta-test features, further establishing the advantage of meta-learning. Experiments demonstrate that our (ML)-L-3 can effectively enhance the generalization ability of the model for unseen domains and can outperform the state-of-the-art methods on four large-scale ReID datasets.

关键词： Training Data privacy computer vision Computational modeling Benchmark testing Data models pattern recognition

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S³ : Learnable Sparse Signal Superdensity for Guided Depth Estimation

<i>S</i><SUP>3</SUP> : Learnable Sparse Signal Superdensity ...

引用

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

作者： Huang, Yu-Kai Liu, Yueh-Cheng Wu, Tsung-Han Su, Hung-Ting Chang, Yu-Cheng Tsou, Tsung-Lin Wang, Yu-An Hsu, Winston H. Natl Taiwan Univ Taipei Taiwan

ISBN: (纸本)9781665445092

Dense depth estimation plays a key role in multiple applications such as robotics, 3D reconstruction, and augmented reality. While sparse signal, e.g., LiDAR and Radar, has been leveraged as guidance for enhancing dense depth estimation, the improvement is limited due to its low density and imbalanced distribution. To maximize the utility from the sparse source, we propose Sparse Signal Superdensity (S-3) technique, which expands the depth value from sparse cues while estimating the confidence of expanded region. The proposed S-3 can be applied to various guided depth estimation approaches and trained end-to-end at different stages, including input, cost volume and output. Extensive experiments demonstrate the effectiveness, robustness, and flexibility of the S-3 technique on LiDAR and Radar signal.

关键词： computer vision Laser radar Three-dimensional displays Costs Estimation Robustness pattern recognition

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Open-book Video Captioning with Retrieve-Copy-Generate Network

Open-book Video Captioning with Retrieve-Copy-Generate Netwo...

引用

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

作者： Zhang, Ziqi Qi, Zhongang Yuan, Chunfeng Shan, Ying Li, Bing Deng, Ying Hu, Weiming Chinese Acad Sci Inst Automat NLPR Beijing Peoples R China Tencent PCG Appl Res Ctr ARC Shenzhen Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China Nanchang Hangkong Univ Sch Aeronaut Mfg Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665445092

In this paper, we convert traditional video captioning task into a new paradigm, i.e., Open-book Video Captioning, which generates natural language under the prompts of video-content-relevant sentences, not limited to the video itself. To address the open-book video captioning problem, we propose a novel Retrieve-Copy-Generate network, where a pluggable video-to-text retriever is constructed to retrieve sentences as hints from the training corpus effectively, and a copy-mechanism generator is introduced to extract expressions from multi-retrieved sentences dynamically. The two modules can be trained end-to-end or separately, which is flexible and extensible. Our framework coordinates the conventional retrieval-based methods with orthodox encoder-decoder methods, which can not only draw on the diverse expressions in the retrieved sentences but also generate natural and accurate content of the video. Extensive experiments on several benchmark datasets show that our proposed approach surpasses the state-of-the-art performance, indicating the effectiveness and promising of the proposed paradigm in the task of video captioning.

关键词： Training computer vision Computational modeling Natural languages Benchmark testing Generators pattern recognition

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Multimodal Prompting with Missing Modalities for Visual recognition

Multimodal Prompting with Missing Modalities for Visual Reco...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yi-Lun Lee Yi-Hsuan Tsai Wei-Chen Chiu Chen-Yu Lee National Yang Ming Chiao Tung University Google

In this paper, we tackle two challenges in multimodal learning for visual recognition: 1) when missing-modality occurs either during training or testing in real-world situations; and 2) when the computation resources are not available to finetune on heavy transformer models. To this end, we propose to utilize prompt learning and mitigate the above two challenges together. Specifically, our modality-missing-aware prompts can be plugged into multimodal transformers to handle general missing-modality cases, while only requiring less than 1% learnable parameters compared to training the entire model. We further explore the effect of different prompt configurations and analyze the robustness to missing modality. Extensive experiments are conducted to show the effectiveness of our prompt learning framework that improves the performance under various missing-modality cases, while alleviating the requirement of heavy model retraining. Code is available. 1 1 https://***/YiLunLee/missing_aware_prompts

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Spk2ImgNet: Learning to Reconstruct Dynamic Scene from Continuous Spike Stream

Spk2ImgNet: Learning to Reconstruct Dynamic Scene from Conti...

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

作者： Zhao, Jing Xiong, Ruiqin Liu, Hangfan Zhang, Jian Huang, Tiejun Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Univ Penn Ctr Biomed Image Comp & Analyt Philadelphia PA 19104 USA Peking Univ Shenzhen Grad Sch Beijing Peoples R China

ISBN: (纸本)9781665445092

The recently invented retina-inspired spike camera has shown great potential for capturing dynamic scenes. Different from the conventional digital cameras that compact the photoelectric information within the exposure interval into a single snapshot, the spike camera produces a continuous spike stream to record the dynamic light intensity variation process. For spike cameras, image reconstruction remains an important and challenging issue. To this end, this paper develops a spike-to-image neural network (Spk2ImgNet) to reconstruct the dynamic scene from the continuous spike stream. In particular, to handle the challenges brought by both noise and high-speed motion, we propose a hierarchical architecture to exploit the temporal correlation of the spike stream progressively. Firstly, a spatially adaptive light inference subnet is proposed to exploit the local temporal correlation, producing basic light intensity estimates of different moments. Then, a pyramid deformable alignment is utilized to align the intermediate features such that the feature fusion module can exploit the long-term temporal correlation, while avoiding undesired motion blur. In addition, to train the network, we simulate the working mechanism of spike camera to generate a large-scale spike dataset composed of spike streams and corresponding ground truth images. Experimental results demonstrate that the proposed network evidently outperforms the state-of-the-art spike camera reconstruction methods.

关键词： Correlation Dynamics Neural networks computer architecture Streaming media Reconstruction algorithms Cameras

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GenHowTo: Learning to Generate Actions and State Transformations from Instructional Videos

GenHowTo: Learning to Generate Actions and State Transformat...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Tomáš Souček Dima Damen Michael Wray Ivan Laptev Josef Sivic CIIRC CTU Czech Institute of Informatics Robotics and Cybernetics at the Czech Technical University in Prague. University of Bristol MBZUAI Mohamed bin Zayed University of Artificial Intelligence.

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We address the task of generating temporally consistent and physically plausible images of actions and object state transformations. Given an input image and a text prompt describing the targeted transformation, our generated images preserve the environment and transform objects in the initial image. Our contributions are threefold. First, we leverage a large body of instructional videos and automatically mine a dataset of triplets of consecutive frames corresponding to initial object states, actions, and resulting object transformations. Second, equipped with this data, we develop and train a conditioned diffusion model dubbed GenHowTo. Third, we evaluate GenHowTo on a variety of objects and actions and show superior performance compared to existing methods. In particular, we introduce a quantitative evaluation where GenHowTo achieves 88% and 74% on seen and unseen interaction categories, respectively, outperforming prior work by a large margin.

关键词： computer vision Computational modeling Transforms Diffusion models pattern recognition Videos

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Brain Decodes Deep Nets

Brain Decodes Deep Nets

引用

conference on computer vision and pattern recognition (cvpr)

作者： Huzheng Yang James Gee Jianbo Shi University of Pennsylvania

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We developed a tool for visualizing and analyzing large pre-trained vision models by mapping them onto the brain, thus exposing their hidden inside. Our innovation arises from a surprising usage of brain encoding: predicting brain fMRI measurements in response to images. We report two findings. First, explicit mapping between the brain and deep-network features across dimensions of space, layers, scales, and channels is crucial. This mapping method, Fac-torTopy, is plug-and-play for any deep-network; with it, one can paint a picture of the network onto the brain (liter-ally!). Second, our visualization shows how different training methods matter: they lead to remarkable differences in hierarchical organization and scaling behavior, growing with more data or network capacity. It also provides in-sight into fine-tuning: how pre-trained models change when adapting to small datasets. We found brain-like hierarchi-cally organized network suffer less from catastrophic for-getting after fine-tuned.

关键词： Training Technological innovation computer vision Image coding Computational modeling Data visualization Organizations

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Learned Scanpaths Aid Blind Panoramic Video Quality Assessment

Learned Scanpaths Aid Blind Panoramic Video Quality Assessme...

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conference on computer vision and pattern recognition (cvpr)

作者： Kanglong Fan Wen Wen Mu Li Yifan Peng Kede Ma City University of Hong Kong Harbin Institute of Technology Shenzhen The University of Hong Kong

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Panoramic videos have the advantage of providing an immersive and interactive viewing experience. Nevertheless, their spherical nature gives rise to various and uncertain user viewing behaviors, which poses significant challenges for panoramic video quality assessment (PVQA). In this work, we propose an end-to-end optimized, blind PVQA method with explicit modeling of user viewing patterns through visual scanpaths. Our method consists of two modules: a scanpath generator and a quality assessor. The scanpath generator is initially trained to predict future scanpaths by minimizing their expected code length and then jointly optimized with the quality assessor for quality prediction. Our blind PVQA method enables direct quality assessment of panoramic images by treating them as videos composed of identical frames. Experiments on three public panoramic image and video quality datasets, encompassing both synthetic and authentic distortions, validate the superiority of our blind PVQA model over existing methods.

关键词： Training Visualization computer vision Distortion Generators Quality assessment pattern recognition

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Context-Based and Diversity-Driven Specificity in Compositional Zero-Shot Learning

Context-Based and Diversity-Driven Specificity in Compositio...

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conference on computer vision and pattern recognition (cvpr)

作者： Yun Li Zhe Liu Hang Chen Lina Yao CSIRO‘s Data61 Bytedance Ltd. Snap Inc.

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object pairs based on a limited set of observed examples. Current CZSL methodologies, despite their advancements, tend to neglect the distinct specificity levels present in attributes. For instance, given images of sliced strawberries, they may fail to prioritize ‘Sliced-Strawberry’ over a generic ‘Red-Strawberry’, despite the former being more informative. They also suffer from ballooning search space when shifting from Close-World (CW) to Open-World (OW) CZSL. To address the issues, we introduce the Context-based and Diversity-driven Specificity learning framework for CZSL (CDS-CZSL). Our framework evaluates the specificity of attributes by considering the diversity of objects they apply to and their related context. This novel approach allows for more accurate predictions by emphasizing specific attribute-object pairs and improves composition filtering in OW-CZSL. We conduct experiments in both CW and OW scenarios, and our model achieves state-of-the-art results across three datasets.

关键词： computer vision Accuracy Zero-shot learning Filtering Computational modeling pattern recognition

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CapsuleRRT: Relationships-aware Regression Tracking via Capsules

CapsuleRRT: Relationships-aware Regression Tracking via Caps...

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

作者： Ma, Ding Wu, Xiangqian Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9781665445092

Regression tracking has gained more and more attention thanks to its easy-to-implement characteristics, while existing regression trackers rarely consider the relationships between the object parts and the complete object. This would ultimately result in drift from the target object when missing some parts of the target object. Recently, Capsule Network (CapsNet) has shown promising results for image classification benefits from its part-object relationships mechanism, while CapsNet is known for its high computational demand even when carrying out simple tasks. Therefore, a primitive adaptation of CapsNet to regression tracking does not make sense, since this will seriously affect speed of a tracker. To solve these problems, we first explore the spatial-temporal relationships endowed by the CapsNet for regression tracking. The entire regression framework, dubbed CapsuleRRT, consists of three parts. One is S-Caps, which captures the spatial relationships between the parts and the object. Meanwhile, a T-Caps module is designed to exploit the temporal relationships within the target. The response of the target is obtained by STCaps Learning. Further, a priorguided capsule routing algorithm is proposed to generate more accurate capsule assignments for subsequent frames. Apart from this, the heavy computation burden in CapsNet is addressed with a knowledge distillation pose matrix compression strategy that exploits more tight and discriminative representation with few samples. Extensive experimental results show that CapsuleRRT performs favorably against state-of-the-art methods in terms of accuracy and speed.

关键词： computer vision Target tracking Meetings Routing pattern recognition Task analysis Image classification

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