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The Devil Is in the Details: Window-based Attention for Image Compression

The Devil Is in the Details: Window-based Attention for Imag...

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

作者： Zou, Renjie Song, Chunfeng Zhang, Zhaoxiang Chinese Acad Sci CASIA Inst Automat Natl Lab Pattern Recognit NLPR Beijing Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China HKISI CAS Ctr Artificial Intelligence & Robot Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Learned image compression methods have exhibited superior rate-distortion performance than classical image compression standards. Most existing learned image compression models are based on Convolutional Neural Networks (CNNs). Despite great contributions, a main drawback of CNN based model is that its structure is not designed for capturing local redundancy, especially the non-repetitive textures, which severely affects the reconstruction quality. Therefore, how to make MI use of both global structure and local texture becomes the core problem for learning-based image compression. Inspired by recent progresses of vision Transformer (ViT) and Swin Transformer, we found that combining the local-aware attention mechanism with the global-related feature learning could meet the expectation in image compression. In this paper, we first extensively study the effects of multiple kinds of attention mechanisms for local features learning, then introduce a more straightforward yet effective window-based local attention block. The proposed window-based attention is very flexible which could work as a plug-and-play component to enhance CNN and Transformer models. Moreover, we propose a novel Symmetrical TransFormer (STF) framework with absolute transformer blocks in the down-sampling encoder and up-sampling decoder. Extensive experimental evaluations have shown that the proposed method is effective and outperforms the state-of-the-art methods. The code is publicly available at https://***/Googolxx/STF.

关键词： Representation learning Image coding Redundancy Rate-distortion Transformer cores Transformers Decoding

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Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification

Farewell to Mutual Information: Variational Distillation for...

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

作者： Tian, Xudong Zhang, Zhizhong Lin, Shaohui Qu, Yanyun Xie, Yuan Ma, Lizhuang East China Normal Univ Shanghai Peoples R China Xiamen Univ Xiamen Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy. Though IB principle has been applied to a wide range of applications, its optimization remains a challenging problem which heavily relies on the accurate estimation of mutual information. In this paper, we present a new strategy, Variational Self-Distillation (VSD), which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it. Under rigorously theoretical guarantee, VSD enables the IB to grasp the intrinsic correlation between representation and label for supervised training. Furthermore, by extending VSD to multi-view learning, we introduce two other strategies, Variational Cross-Distillation (VCD) and Variational Mutual-Learning (VML), which significantly improve the robustness of representation to viewchanges by eliminating view-specific and task-irrelevant information. To verify our theoretically grounded strategies, we apply our approaches to cross-modal person Re-ID, and conduct extensive experiments, where the superior performance against state-of-the-art methods are demonstrated. Our intriguing findings highlight the need to rethink the way to estimate mutual information.

关键词： Training Correlation Fitting Redundancy Estimation Robustness pattern recognition

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Transductive Unbiased Embedding for Zero-Shot Learning 31

Transductive Unbiased Embedding for Zero-Shot Learning

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Song, Jie Shen, Chengchao Yang, Yezhou Liu, Yang Song, Mingli Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Arizona State Univ Tempe AZ USA Alibaba Grp Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538664209

Most existing Zero-Shot Learning (ZSL) methods have the strong bias problem, in which instances of unseen (target) classes tend to be categorized as one of the seen (source) classes. So they yield poor performance after being deployed in the generalized ZSL settings. In this paper, we propose a straightforward yet effective method named Quasi-Fully Supervised Learning (QFSL) to alleviate the bias problem. Our method follows the way of transductive learning, which assumes that both the labeled source images and unlabeled target images are available for training. In the semantic embedding space, the labeled source images are mapped to several fixed points specified by the source categories, and the unlabeled target images are forced to be mapped to other points specified by the target categories. Experiments conducted on AwA2, CUB and SUN datasets demonstrate that our method outperforms existing state-of-the-art approaches by a huge margin of 9.3 similar to 24.5% following generalized ZSL settings, and by a large margin of 0.2 similar to 16.2% following conventional ZSL settings.

关键词： Visualization Semantics Training Target recognition Image recognition Data models Supervised learning

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Improving Multi-Target Multi-Camera Tracking by Track Refinement and Completion

Improving Multi-Target Multi-Camera Tracking by Track Refine...

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

作者： Specker, Andreas Florin, Lucas Cormier, Mickael Beyerer, Juergen Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer Ctr Machine Learning St Augustin Germany

ISBN: (纸本)9781665487399

Multi-camera tracking of vehicles on a city-wide level is a core component of modern traffic monitoring systems. For this task, single-camera tracking failures are the most common causes of errors concerning automatic multi-target multi-camera tracking systems. To address these problems, we propose several modules that aim at improving single-camera tracklets, e.g., appearance-based tracklet splitting, single-camera clustering, and track completion. After these track refinement steps, hierarchical clustering is used to associate the enhanced single-camera tracklets. During this stage, we leverage vehicle re-identification features as well as prior knowledge about the scene's topology. Last, the proposed track completion strategy is adopted for the cross-camera association task to obtain the final multi-camera tracks. Our method proves itself competitive: With it, we achieved 4th place in track 1 of the 2022 AI City Challenge.

关键词： computer vision conferences Urban areas Topology pattern recognition Task analysis Artificial intelligence

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Synthesizing 3D Shapes from Silhouette Image Collections using Multi-projection Generative Adversarial Networks 32

Synthesizing 3D Shapes from Silhouette Image Collections usi...

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Xiao Dong, Yue Peers, Pieter Tong, Xin Univ Sci & Technol China Hefei Peoples R China Microsoft Res Asia Beijing Peoples R China Coll William & Mary Williamsburg VA 23187 USA

ISBN: (纸本)9781728132938

We present a new weakly supervised learning-based method for generating novel category-specific 3D shapes from unoccluded image collections. Our method is weakly supervised and only requires silhouette annotations from unoccluded, category-specific objects. Our method does not require access to the object's 3D shape, multiple observations per object from different views, intra-image pixel correspondences, or any view annotations. Key to our method is a novel multi-projection generative adversarial network (MP-GAN) that trains a 3D shape generator to be consistent with multiple 2D projections of the 3D shapes, and without direct access to these 3D shapes. This is achieved through multiple discriminators that encode the distribution of 2D projections of the 3D shapes seen from a different views. Additionally, to determine the view information for each silhouette image, we also train a view prediction network on visualizations of 3D shapes synthesized by the generator. We iteratively alternate between training the generator and training the view prediction network. We validate our multi-projection GAN on both synthetic and real image datasets. Furthermore, we also show that multi-projection GANs can aid in learning other high-dimensional distributions from lower dimensional training datasets, such as material-class specific spatially varying reflectance properties from images.

关键词： 3D from Multiview and Sensors Physics-based vision and Shape-from-X vision + Graphics

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Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection

Bayesian Nonparametric Submodular Video Partition for Robust...

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

作者： Sapkota, Hitesh Yu, Qi Rochester Inst Technol Rochester NY 14623 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Multiple-instance learning (MIL) provides an effective way to tackle the video anomaly detection problem by modeling it as a weakly supervised problem as the labels are usually only available at the video level while missing for frames due to expensive labeling cost. We propose to conduct novel Bayesian non-parametric submodular video partition (BN-SVP) to significantly improve MIL model training that can offer a highly reliable solution for robust anomaly detection in practical settings that include outlier segments or multiple types of abnormal events. BN-SVP essentially performs dynamic non-parametric hierarchical clustering with an enhanced self-transition that groups segments in a video into temporally consistent and semantically coherent hidden states that can be naturally interpreted as scenes. Each segment is assumed to be generated through a non-parametric mixture process that allows variations of segments within the same scenes to accommodate the dynamic and noisy nature of many real-world surveillance videos. The scene and mixture component assignment of BN-SVP also induces a pairwise similarity among segments, resulting in non-parametric construction of a submodular set function. Integrating this function with an MIL loss effectively exposes the model to a diverse set of potentially positive instances to improve its training. A greedy algorithm is developed to optimize the submodular function and support efficient model training. Our theoretical analysis ensures a strong performance guarantee of the proposed algorithm. The effectiveness of the proposed approach is demonstrated over multiple real-world anomaly video datasets with robust detection performance.

关键词： Training Upper bound Surveillance Bayes methods pattern recognition Partitioning algorithms Noise measurement

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A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes

A Comprehensive Study of Image Classification Model Sensitiv...

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

作者： Moayeri, Mazda Pope, Phillip Balaji, Yogesh Feizi, Soheil Univ Maryland College Pk MD 20742 USA NVIDIA Santa Clara CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

While datasets with single-label supervision have propelled rapid advances in image classification, additional annotations are necessary in order to quantitatively assess how models make predictions. To this end, for a subset of ImageNet samples, we collect segmentation masks for the entire object and 18 informative attributes. We call this dataset RIVAL10 (RIch Visual Attributes with Localization), consisting of roughly 26k instances over 10 classes. Using RIVAL10, we evaluate the sensitivity of a broad set of models to noise corruptions in foregrounds, backgrounds and attributes. In our analysis, we consider diverse state-of-the-art architectures (ResNets, Transformers) and training procedures (CLIP, SimCLR, DeiT, Adversarial Training). We find that, somewhat surprisingly, in ResNets, adversarial training makes models more sensitive to the background compared to foreground than standard training. Similarly, contrastively-trained models also have lower relative foreground sensitivity in both transformers and ResNets. Lastly, we observe intriguing adaptive abilities of transformers to increase relative foreground sensitivity as corruption level increases. Using saliency methods, we automatically discover spurious features that drive the background sensitivity of models and assess alignment of saliency maps with foregrounds. Finally, we quantitatively study the attribution problem for neural features by comparing feature saliency with ground-truth localization of semantic attributes.

关键词： Training Location awareness Adaptation models Visualization computer vision Sensitivity Annotations

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Topology Reconstruction of Tree-like Structure in Images via Structural Similarity Measure and Dominant Set Clustering 32

Topology Reconstruction of Tree-like Structure in Images via...

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Xie, Jianyang Zhao, Yitian Liu, Yonghuai Su, Pan Zhao, Yifan Cheng, Jun Zheng, Yalin Liu, Jiang Chinese Acad Sci Ningbo Inst Ind Technol Beijing Peoples R China Edge Hill Univ Dept Comp Sci Ormskirk England Cranfield Univ Sch Aerosp Transport & Mfg Cranfield Beds England Univ Liverpool Dept Eye & Vis Sci Liverpool Merseyside England

ISBN: (纸本)9781728132938

The reconstruction and analysis of tree-like topological structures in the biomedical images is crucial for biologists and surgeons to understand biomedical conditions and plan surgical procedures. The underlying tree-structure topology reveals how different curvilinear components are anatomically connected to each other. Existing automated topology reconstruction methods have great difficulty in identifying the connectivity when two or more curvilinear components cross or bifurcate, due to their projection ambiguity, imaging noise and low contrast. In this paper, we propose a novel curvilinear structural similarity measure to guide a dominant-set clustering approach to address this indispensable issue. The novel similarity measure takes into account both intensity and geometric properties in representing the curvilinear structure locally and globally, and group curvilinear objects at crossover points into different connected branches by dominant-set clustering. The proposed method is applicable to different imaging modalities, and quantitative and qualitative results on retinal vessel, plant root, and neuronal network datasets show that our methodology is capable of advancing the current state-of-the-art techniques.

关键词： Biological and Cell Microscopy Categorization Image and Video Synthesis Low-level vision Medical recognition: Detection Retrieval Segm

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The STVchrono Dataset: Towards Continuous Change recognition in Time

The STVchrono Dataset: Towards Continuous Change Recognition...

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

作者： Sun, Yanjun Qiu, Yue Khan, Mariia Matsuzawa, Fumiya Iwata, Kenji Natl Inst Adv Ind Sci & Technol Tokyo Japan Keio Univ Tokyo Japan Edith Cowan Univ Joondalup Australia

ISBN: (纸本)9798350353006

Recognizing continuous changes offers valuable insights into past historical events, supports current trend analysis, and facilitates future planning. This knowledge is crucial for a variety of fields, such as meteorology and agriculture, environmental science, urban planning and construction, tourism, and cultural preservation. Currently available datasets in the field of scene change understanding primarily concentrate on two main tasks: the detection of changed regions within a scene and the linguistic description of the change content. Existing datasets focus on recognizing discrete changes, such as adding or deleting an object from two images, and largely rely on artificially generated images. Consequently, the existing change understanding methods primarily focus on identifying distinct object differences, overlooking the importance of continuous, gradual changes occurring over extended time intervals. To address the above issues, we propose a novel bench-mark dataset, STVchrono, targeting the localization and description of long-term continuous changes in real-world scenes. The dataset consists of 71,900 photographs from Google Street View API taken over an 18-year span across 50 cities all over the world. Our STVchrono dataset is designed to support real-world continuous change recognition and description in both image pairs and extended image sequences, while also enabling the segmentation of changed regions. We conduct experiments to evaluate state-of-the-art methods on continuous change description and segmentation, as well as multimodal Large Language Models for describing changes. Our findings reveal that even the most advanced methods lag human performance, emphasizing the need to adapt them to continuously changing real-world scenarios. We hope that our benchmark dataset will further facilitate the research of temporal change recognition in a dynamic world. The STVchrono dataset is available at STVchrono Dataset.

关键词： Image segmentation

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Flow Guided Transformable Bottleneck Networks for Motion Retargeting

Flow Guided Transformable Bottleneck Networks for Motion Ret...

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

作者： Ren, Jian Chai, Menglei Woodford, Oliver J. Olszewski, Kyle Tulyakov, Sergey Snap Inc Santa Monica CA 90405 USA

ISBN: (纸本)9781665445092

Human motion retargeting aims to transfer the motion of one person in a "driving" video or set of images to another person. Existing efforts leverage a long training video from each target person to train a subject-specific motion transfer model. However, the scalability of such methods is limited, as each model can only generate videos for the given target subject, and such training videos are labor-intensive to acquire and process. Few-shot motion transfer techniques, which only require one or a few images from a target, have recently drawn considerable attention. Methods addressing this task generally use either 2D or explicit 3D representations to transfer motion, and in doing so, sacrifice either accurate geometric modeling or the flexibility of an end-to-end learned representation. Inspired by the Transformable Bottleneck Network, which renders novel views and manipulations of rigid objects, we propose an approach based on an implicit volumetric representation of the image content, which can then be spatially manipulated using volumetric flow fields. We address the challenging question of how to aggregate information across different body poses, learning flow fields that allow for combining content from the appropriate regions of input images of highly non-rigid human subjects performing complex motions into a single implicit volumetric representation. This allows us to learn our 3D representation solely from videos of moving people. Armed with both 3D object understanding and end-to-end learned rendering, this categorically novel representation delivers state-of-the-art image generation quality, as shown by our quantitative and qualitative evaluations.

关键词： Training computer vision Three-dimensional displays Image synthesis Scalability Pose estimation Rendering (computer graphics)

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