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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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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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Bangla Handwritten Character recognition using vision Transformer

Bangla Handwritten Character Recognition using Vision Transf...

引用

International conference on Electrical, computer and Communication Engineering (ECCE)

作者： Tasmi Khair Tapu Farhan Faiaz Anika Nawer Sadia Rahman Payel Department of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Handwritten character recognition (HCR) is a crucial field of research, especially for complex scripts like Bangla, which include a variety of detailed and diverse characters. Hand written character recognition has mostly been approached using Convolutional Neural Networks (CNN) and transfer learning. Though researchers have been actively exploring handwritten character recognition, only a few of them have explored the potential of vision Transformers (ViT), especially for recognizing Bangla handwritten characters. vision Transformer is a recent addition to deep neural networks, offering strong performance with lower computational power than existing methods. This paper introduces a vision transformer with data augmentation techniques to tackle Bangla Handwritten Character recognition (BHCR) using vision Transformer (ViT) model with data augmentation techniques. Traditional convolutional neural networks (CNN) are focused on the local pattern while ViT is focused on capturing long-range dependencies by processing images as sequences of patches. The proposed vision transformer model with 8 transformer layers was trained and evaluated on the CMATERdb 3.1.2 dataset which consists of 15,000 images representing 50 Bangla handwritten characters. Our proposed ViT model with data augmentation techniques, achieved an outstanding accuracy of 95.27% outperforming many existing Bangla handwritten character recognition methods. These remarkable outcomes highlight the importance of data augmentation and the efficiency of vision Transformers in handling complex scripts like Bangla.

关键词： Hands computer vision Handwriting recognition Computational modeling Transfer learning Transformers Data augmentation Data models Character recognition Convolutional neural networks

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DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks

DISCO: Dynamic and Invariant Sensitive Channel Obfuscation f...

引用

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

作者： Singh, Abhishek Chopra, Ayush Garza, Ethan Zhang, Emily Vepakomma, Praneeth Sharma, Vivek Raskar, Ramesh MIT 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Med Sch Boston MA 02115 USA

ISBN: (纸本)9781665445092

Recent deep learning models have shown remarkable performance in image classification. While these deep learning systems are getting closer to practical deployment, the common assumption made about data is that it does not carry any sensitive information. This assumption may not hold for many practical cases, especially in the domain where an individual's personal information is involved, like healthcare and facial recognition systems. We posit that selectively removing features in this latent space can protect the sensitive information and provide better privacy-utility trade-off. Consequently, we propose DISCO which learns a dynamic and data driven pruning filter to selectively obfuscate sensitive information in the feature space. We propose diverse attack schemes for sensitive inputs & attributes and demonstrate the effectiveness of DISCO against state-of-the-art methods through quantitative and qualitative evaluation. Finally, we also release an evaluation benchmark dataset of 1 million sensitive representations to encourage rigorous exploration of novel attack and defense schemes at https://***/splitlearning/InferenceBenchmark.

关键词： Deep learning Privacy computer vision Face recognition Collaboration Medical services Benchmark testing

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

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

引用

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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NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2024 Challenge on Stereo Image Super-Resolution: Metho...

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Longguang Wang Yulan Guo Juncheng Li Hongda Liu Yang Zhao Yingqian Wang Zhi Jin Shuhang Gu Radu Timofte

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper summarizes the 3rd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of this challenge is to super-resolve a low-resolution stereo image pair to a high-resolution one with a magnification factor of × 4 under a limited computational budget. Compared with single image SR, the major challenge of this challenge lies in how to exploit additional information in another viewpoint and how to maintain stereo consistency in the results. This challenge has 2 tracks, including one track on bicubic degradation and one track on real degradations. In total, 108 and 70 participants were successfully registered for each track, respectively. In the test phase, 14 and 13 teams successfully submitted valid results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR.

关键词： Degradation computer vision conferences Superresolution Benchmark testing pattern recognition

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Partial Person Re-identification with Part-Part Correspondence Learning

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

引用

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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Efficient Skeleton-Based Action recognition for Real-Time Embedded Systems

Efficient Skeleton-Based Action Recognition for Real-Time Em...

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IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Nadhira Noor Fabianaugie Jametoni Jinbeom Kim Hyunsu Hong In Kyu Park Dept. of Electrical and Computer Engineering Inha University Incheon Korea Finedigital Inc Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Action recognition is vital for various real-world applications, yet its implementation on embedded systems or edge devices faces challenges due to limited computing and memory resources. Our goal is to facilitate lightweight action recognition on embedded systems by utilizing skeleton-based techniques, which naturally require less computing and memory resources. To achieve this, we propose innovative methodologies and optimizations tailored for embedded deployment, including post-training quantization, optimized model architectures, and efficient resource utilization. By enabling real-time and lightweight action recognition on resource-constrained embedded systems, our research opens up new possibilities for applications in areas like autonomous surveillance, driving, and indoor safety monitoring.

关键词： Embedded systems Accuracy Quantization (signal) Surveillance Memory management Network architecture Real-time systems

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DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring Dataset for Real-World Outdoor Scenarios

DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring ...

引用

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

作者： Bolten, Tobias Pohle-Froehlich, Regina Toennies, Klaus D. Hsch Niederrhein Inst Pattern Recognit Krefeld Germany Univ Magdeburg Dept Simulat & Graph Magdeburg Germany

ISBN: (纸本)9781665448994

Neuromorphic vision sensors are biologically inspired devices which differ fundamentally from well known frame-based sensors. Even though developments in this research area are increasing, applications that rely entirely on event cameras are still relatively rare. This becomes particularly clear when considering real outdoor scenarios apart from laboratory conditions. One obstacle to the development of event-based vision applications in this context may be the lack of labeled datasets for algorithm development and evaluation. Therefore we describe a recording setting of a DVS-based long time monitoring of an urban public area and provide labeled DVS data that also contain effects of environmental outdoor influences recorded in this process. We also describe the processing chain used for label generation, as well as results from a performed denoising benchmark utilizing various spatio-temporal event stream filters. The dataset contains almost 7 hours of real world outdoor event-data with approximate to 47k labeled regions of interest and can be downloaded at http://***/DVS-OUTLAB/

关键词： Privacy Rain Power demand Neuromorphics Noise reduction Pipelines vision sensors

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VirFace: Enhancing Face recognition via Unlabeled Shallow Data

VirFace: Enhancing Face Recognition via Unlabeled Shallow Da...

引用

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

作者： Li, Wenyu Guo, Tianchu Li, Pengyu Chen, Binghui Wang, Biao Zuo, Wangmeng Zhang, Lei Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Recently, how to exploit unlabeled data for training face recognition models has been attracting increasing attention. However, few works consider the unlabeled shallow data(1) in real-world scenarios. The existing semi-supervised face recognition methods that focus on generating pseudo labels or minimizing softmax classification probabilities of the unlabeled data do not work very well on the unlabeled shallow data. It is still a challenge on how to effectively utilize the unlabeled shallow face data to improve the performance of face recognition. In this paper, we propose a novel face recognition method, named VirFace, to effectively exploit the unlabeled shallow data for face recognition. VirFace consists of VirClass and VirInstance. Specifically, VirClass enlarges the inter-class distance by injecting the unlabeled data as new identities, while VirInstance produces virtual instances sampled from the learned distribution of each identity to further enlarge the inter-class distance. To the best of our knowledge, we are the first to tackle the problem of unlabeled shallow face data. Extensive experiments have been conducted on both the small- and large-scale datasets, e.g. LFW and IJB-C, etc, demonstrating the superiority of the proposed method.

关键词： Training computer vision Face recognition Benchmark testing Data models

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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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