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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

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DER: Dynamically Expandable Representation for Class Incremental Learning

DER: Dynamically Expandable Representation for Class Increme...

引用

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

作者： Yan, Shipeng Xie, Jiangwei He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781665445092

We address the problem of class incremental learning, which is a core step towards achieving adaptive vision intelligence. In particular, we consider the task setting of incremental learning with limited memory and aim to achieve better stability-plasticity trade-off. To this end, we propose a novel two-stage learning approach that utilizes a dynamically expandable representation for more effective incremental concept modeling. Specifically, at each incremental step, we freeze the previously learned representation and augment it with additional feature dimensions from a new learnable feature extractor. This enables us to integrate new visual concepts with retaining learned knowledge. We dynamically expand the representation according to the complexity of novel concepts by introducing a channel-level mask-based pruning strategy. Moreover, we introduce an auxiliary loss to encourage the model to learn diverse and discriminate features for novel concepts. We conduct extensive experiments on the three class incremental learning benchmarks and our method consistently outperforms other methods with a large margin.(1)

关键词： Visualization computer vision Adaptation models Benchmark testing Feature extraction pattern recognition Complexity theory

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Reinforced Attention for Few-Shot Learning and Beyond

Reinforced Attention for Few-Shot Learning and Beyond

引用

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

作者： Hong, Jie Fang, Pengfei Li, Weihao Zhang, Tong Simon, Christian Harandi, Mehrtash Petersson, Lars Australian Natl Univ Canberra ACT Australia Data61 CSIRO Canberra ACT Australia Ecole Polytech Fed Lausanne Lausanne Switzerland Monash Univ Clayton Vic Australia

ISBN: (纸本)9781665445092

Few-shot learning aims to correctly recognize query samples from unseen classes given a limited number of support samples, often by relying on global embeddings of images. In this paper, we propose to equip the backbone network with an attention agent, which is trained by reinforcement learning. The policy gradient algorithm is employed to train the agent towards adaptively localizing the representative regions on feature maps over time. We further design a reward function based on the prediction of the held-out data, thus helping the attention mechanism to generalize better across the unseen classes. The extensive experiments show, with the help of the reinforced attention, that our embedding network has the capability to progressively generate a more discriminative representation in few-shot learning. Moreover, experiments on the task of image classification also show the effectiveness of the proposed design.

关键词： computer vision Image recognition Computational modeling Reinforcement learning Prediction algorithms Data models pattern recognition

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Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble

Divide and Conquer: High-Resolution Industrial Anomaly Detec...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Blaž Rolih Dick Ameln Ashwin Vaidya Samet Akcay Faculty of Computer and Information Science University of Ljubljana Intel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Industrial anomaly detection is an important task within computer vision with a wide range of practical use cases. The small size of anomalous regions in many real-world datasets necessitates processing the images at a high resolution. This frequently poses significant challenges concerning memory consumption during the model training and inference stages, leaving some existing methods impractical for widespread adoption. To overcome this challenge, we present the tiled ensemble approach, which reduces memory consumption by dividing the input images into a grid of tiles and training a dedicated model for each tile location. The tiled ensemble is compatible with any existing anomaly detection model without the need for any modification of the underlying architecture. By introducing overlapping tiles, we utilize the benefits of traditional stacking ensembles, leading to further improvements in anomaly detection capabilities beyond high resolution alone. We perform a comprehensive analysis using diverse underlying architectures, including Padim, PatchCore, FastFlow, and Reverse Distillation, on two standard anomaly detection datasets: MVTec and VisA. Our method demonstrates a notable improvement across setups while remaining within GPU memory constraints, consuming only as much GPU memory as a single model needs to process a single tile. 1 2

关键词： Training computer vision Image resolution Tiles Memory management Stacking Graphics processing units

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SSAN: Separable Self-Attention Network for Video Representation Learning

SSAN: Separable Self-Attention Network for Video Representat...

引用

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

作者： Guo, Xudong Guo, Xun Lu, Yan Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China MSRA Beijing Peoples R China

ISBN: (纸本)9781665445092

Self-attention has been successfully applied to video representation learning due to the effectiveness of modeling long range dependencies. Existing approaches build the dependencies merely by computing the pairwise correlations along spatial and temporal dimensions simultaneously. However;spatial correlations and temporal correlations represent different contextual information of scenes and temporal reasoning. Intuitively, learning spatial contextual information first will benefit temporal modeling. In this paper;we propose a separable self-attention (SSA) module, which models spatial and temporal correlations sequentially so that spatial contexts can be efficiently used in temporal modeling. By adding SSA module into 21) CNN, we build a SSA network (SSAN) for video representation learning. On the task of video action recognition, our approach outperforms state-of:the-art methods on Something Something and Kinetics-400 datasets. Our models often outperform counterparts with shallower network and fewer modalities. We further verify the semantic learning ability of our method in visual-language task of video retrieval, which showcases the homogeneity of video representations and text embeddings. On MSR-VIT and Youcook2 dowsers, video representations learnt by SSA significantly improve the state-of-the-art performance.

关键词： computer vision Correlation Pairwise error probability Computational modeling Semantics Cognition pattern recognition

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Partial Person Re-identification with Part-Part Correspondence Learning

Partial Person Re-identification with Part-Part Corresponden...

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

作者： He, Tianyu Shen, Xu Huang, Jianqiang Chen, Zhibo Hua, Xian-Sheng Alibaba Grp DAMO Acad Hangzhou Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781665445092

Driven by the success of deep learning, the last decade has seen rapid advances in person re-identification (re-ID). Nonetheless, most of approaches assume that the input is given with the fulfillment of expectations, while imperfect input remains rarely explored to date, which is a non-trivial problem since directly apply existing methods without adjustment can cause significant performance degradation. In this paper, we focus on recognizing partial (flawed) input with the assistance of proposed Part-Part Correspondence Learning (PPCL), a self-supervised learning framework that learns correspondence between image patches without any additional part-level supervision. Accordingly, we propose Part-Part Cycle (PP-Cycle) constraint and Part-Part Triplet (PP-Triplet) constraint that exploit the duality and uniqueness between corresponding image patches respectively. We verify our proposed PPCL on several partial person re-ID benchmarks. Experimental results demonstrate that our approach can surpass previous methods in terms of the standard evaluation metric.

关键词： Measurement Degradation Deep learning computer vision Image recognition Benchmark testing pattern recognition

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Predicting Human Scanpaths in Visual Question Answering

Predicting Human Scanpaths in Visual Question Answering

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

作者： Chen, Xianyu Jiang, Ming Zhao, Qi Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781665445092

Attention has been an important mechanism for both humans and computer vision systems. While state-of-the-art models to predict attention focus on estimating a static probabilistic saliency map with free-viewing behavior, real-life scenarios are filled with tasks of varying types and complexities, and visual exploration is a temporal process that contributes to task performance. To bridge the gap, we conduct a first study to understand and predict the temporal sequences of eye fixations (a.k.a. scanpaths) during performing general tasks, and examine how scanpaths affect task performance. We present a new deep reinforcement learning method to predict scanpaths leading to different performances in visual question answering. Conditioned on a task guidance map, the proposed model learns question-specific attention patterns to generate scanpaths. It addresses the exposure bias in scanpath prediction with self-critical sequence training and designs a Consistency-Divergence loss to generate distinguishable scanpaths between correct and incorrect answers. The proposed model not only accurately predicts the spatio-temporal patterns of human behavior in visual question answering, such as fixation position, duration, and order, but also generalizes to free-viewing and visual search tasks, achieving human-level performance in all tasks and significantly outperforming the state of the art.

关键词： Training Visualization computer vision Reinforcement learning Predictive models Tools Knowledge discovery

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Dictionary-guided Scene Text recognition

Dictionary-guided Scene Text Recognition

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

作者： Nguyen Nguyen Thu Nguyen Vinh Tran Minh-Triet Tran Thanh Duc Ngo Thien Huu Nguyen Minh Hoai VinAI Res Hanoi Vietnam VNU HCM Univ Informat Technol Hanoi Vietnam VNU HCM Univ Sci Hanoi Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam Univ Oregon Eugene OR 97403 USA SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9781665445092

Language prior plays an important role in the way humans detect and recognize text in the wild. Current scene text recognition methods do use lexicons to improve recognition performance, but their naive approach of casting the output into a dictionary word based purely on the edit distance has many limitations. In this paper, we present a novel approach to incorporate a dictionary in both the training and inference stage of a scene text recognition system. We use the dictionary to generate a list of possible outcomes and find the one that is most compatible with the visual appearance of the text. The proposed method leads to a robust scene text recognition model, which is better at handling ambiguous cases encountered in the wild, and improves the overall performance of state-of-the-art scene text spotting frameworks. Our work suggests that incorporating language prior is a potential approach to advance scene text detection and recognition methods. Besides, we contribute VinText, a challenging scene text dataset for Vietnamese, where some characters are equivocal in the visual form due to accent symbols. This dataset will serve as a challenging benchmark for measuring the applicability and robustness of scene text detection and recognition algorithms.

关键词： Training Visualization computer vision Casting Dictionaries Codes Text recognition

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StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation

StyleSpace Analysis: Disentangled Controls for StyleGAN Imag...

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

作者： Wu, Zongze Lischinski, Dani Shechtman, Eli Hebrew Univ Jerusalem Jerusalem Israel Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first show that StyleSpace, the space of channel-wise style parameters, is significantly more disentangled than the other intermediate latent spaces explored by previous works. Next, we describe a method for discovering a large collection of style channels, each of which is shown to control a distinct visual attribute in a highly localized and disentangled manner. Third, we propose a simple method for identifying style channels that control a specific attribute, using a pretrained classifier or a small number of example images. Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works. To show this, we make use of a newly proposed Attribute Dependency metric. Finally, we demonstrate the applicability of StyleSpace controls to the manipulation of real images. Our findings pave the way to semantically meaningful and well-disentangled image manipulations via simple and intuitive interfaces.

关键词： Measurement Visualization computer vision Analytical models Image synthesis computer architecture Aerospace electronics

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3D Human Action Representation Learning via Cross-View Consistency Pursuit

3D Human Action Representation Learning via Cross-View Consi...

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

作者： Li, Linguo Wang, Minsi Ni, Bingbing Wang, Hang Yang, Jiancheng Zhang, Wenjun Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging multiview complementary supervision signal. CrosSCLR consists of both single-view contrastive learning (Skeleton-CLR) and cross-view consistent knowledge mining (CVC-KM) modules, integrated in a collaborative learning manner. It is noted that CVC-KM works in such a way that high-confidence positive/negative samples and their distributions are exchanged among views according to their embedding similarity, ensuring cross-view consistency in terms of contrastive context, i.e., similar distributions. Extensive experiments show that CrosSCLR achieves remarkable action recognition results on NTU-60 and NTU-120 dmasets under unsupervised settings, with observed higherquality action representations. Our code is available at https://***/Linguoli/CrosSCLR.

关键词： computer vision Three-dimensional displays Codes Collaborative work pattern recognition

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Adaptive Rank Estimate in Robust Principal Component Analysis

Adaptive Rank Estimate in Robust Principal Component Analysi...

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

作者： Xu, Zhengqin He, Rui Xie, Shoulie Wu, Shiqian Wuhan Univ Sci & Technol Sch Machinery & Automat Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Inst Robot & Intelligent Syst Wuhan Hubei Peoples R China Inst Infocomm Res A STAR Signal Proc RF & Opt Dept Singapore Singapore

ISBN: (纸本)9781665445092

Robust principal component analysis (RPCA) and its variants have gained wide applications in computer vision. However, these methods either involve manual adjustment of some parameters, or require the rank of a low-rank mafrix to be known a prior. In this paper, an adaptive rank estimate based RPCA (ARE-RPCA) is proposed, which adaptively assigns weights on different singular values via rank estimation. More specifically, we study the characteristics of the low-rank mafrix, and develop an improved Gerschgorin disk theorem to estimate the rank of the low-rank matrix accurately. Furthermore in view of the issue occurred in the Gerschgorin disk theorem that adjustment factor need to be manually pre-defined, an adaptive setting method, which greatly facilitates the practical implementation of the rank estimation, is presented. Then, the weights of singular values in the nuclear norm are updated adaptively based on iteratively estimated rank, and the resultant low-rank matrix is close to the target. Experimental results show that the proposed ARE-RPCA outperforms the state-of-the-art methods in various complex scenarios.

关键词： computer vision Estimation Manuals pattern recognition Sparse matrices Principal component analysis

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