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

共 52937 条记录，以下是4401-4410 订阅

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Robust Representation Learning with Feedback for Single Image Deraining

Robust Representation Learning with Feedback for Single Imag...

引用

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

作者： Chen, Chenghao Li, Hao Shanghai Jiao Tong Univ SJTU Dept Automat Shanghai 200240 Peoples R China Ecole Ingenieurs SJTU ParisTech SPEIT Shanghai 200240 Peoples R China

ISBN: (纸本)9781665445092

A deraining network can be interpreted as a conditional generator that aims at removing rain streaks from image. Most existing image deraining methods ignore model errors caused by uncertainty that reduces embedding quality. Unlike existing image deraining methods that embed low-quality features into the model directly, we replace low-quality features by latent high-quality features. The spirit of closed-loop feedback in the automatic control field is borrowed to obtain latent high-quality features. A new method for error detection and feature compensation is proposed to address model errors. Extensive experiments on benchmark datasets as well as specific real datasets demonstrate that the proposed method outperforms recent state-of-the-art methods. Code is available at: https://***/LI-Hao-SJTU/DerainRLNet

关键词： computer vision Uncertainty Rain Codes Feature extraction Generators pattern recognition

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Pseudo Labels Refinement with Stable Cluster Reconstruction for Unsupervised Re-identification 6th

Pseudo Labels Refinement with Stable Cluster Reconstruction ...

引用

6th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Liu, Zhenyu Lian, Jiawei Wu, Jiahua Wang, Da-Han Wu, Yun Zhu, Shunzhi Ge, Dewu Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understan Xiamen 361024 Peoples R China Xiamen KEYTOP Commun Technol Co Xiamen 361024 Peoples R China

ISBN: (纸本)9789819984619;9789819984626

Most existing unsupervised re-identification uses a clustering-based approach to generate pseudo-labels as supervised signals, allowing deep neural networks to learn discriminative representations without annotations. However, drawbacks in clustering algorithms and the absence of discriminatory ability early in training limit better performance seriously. A severe problem arises from path dependency, wherein noisy samples rarely have a chance to escape from their assigned clusters during iterative training. To tackle this challenge, we propose a novel label refinement strategy based on the stable cluster reconstruction. Our approach contains twomodules, the stable cluster reconstruction (SCR) module and the similarity recalculate (SR) module. It reconstructs more stable clusters and re-evaluates the relationship between samples and clearer cluster representatives, providing complementary information for pseudo labels at the instance level. Our proposed approach effectively improves unsupervised reID performance, achieving state-of-the-art performance on four benchmark datasets. Specifically, our method achieves 46.0% and 39.1% mAP on the challenging dataset VeRi776 and MSMT17.

关键词： Re-identification Unsupervised learning Label refinement

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Research on computer vision System for Intelligent Management of Football Stadium Based on Internet of Things 2

Research on Computer Vision System for Intelligent Managemen...

引用

2nd ieee International conference on Sensors, Electronics and computer Engineering, ICSECE 2024

作者： Zhang, Yongxiang Wuchang Shouyi University Wuhan China

ISBN: (纸本)9798350373646

This paper proposes an intelligent management computer vision system based on the Internet of Things for the special needs of football stadiums. The system integrates advanced image processing algorithms and computer system architecture, aiming to realize the functions of all-round monitoring, audience behavior analysis and security early warning inside and outside the football stadium. The core of the system includes a network of high-definition cameras, edge computing nodes and a central server. Cameras are responsible for capturing the environment inside and outside the venue in real time, and initial data processing is carried out through edge computing nodes to reduce data transmission latency. The central server is equipped with advanced image recognition algorithms, which can identify player movements, spectator density distribution, and abnormal behavior, such as trespassing, crowd gathering, etc., and issue instant alerts. In addition, the system also has automated ticket management, facility maintenance reminders and energy monitoring functions. The experimental results show that the system significantly improves the management efficiency and safety of football stadiums, especially during large-scale events, and can effectively prevent and respond to various emergencies. At the same time, through intelligent analysis of audience behavior, the system can also provide decision support for venue managers, optimize resource allocation and enhance the spectator experience. © 2024 ieee.

关键词： Resource allocation

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Image Super-Resolution with Non-Local Sparse Attention

Image Super-Resolution with Non-Local Sparse Attention

引用

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

作者： Mei, Yiqun Fan, Yuchen Zhou, Yuqian Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781665445092

Both Non-Local (NL) operation and sparse representation are crucial for Single Image Super-Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non-Local Sparse Attention (NLSA) with dynamic sparse attention pattern. NLSA is designed to retain long-range modeling capability from NL operation while enjoying robustness and high-efficiency of sparse representation. Specifically, NLSA rectifies non-local attention with spherical locality sensitive hashing (LSH) that partitions the input space into hash buckets of related features. For every query signal, NLSA assigns a bucket to it and only computes attention within the bucket. The resulting sparse attention prevents the model from attending to locations that are noisy and less-informative, while reducing the computational cost from quadratic to asymptotic linear with respect to the spatial size. Extensive experiments validate the effectiveness and efficiency of NLSA. With a few non-local sparse attention modules, our architecture, called non-local sparse network (NLSN), reaches state-of-the-art performance for SISR quantitatively and qualitatively.

关键词： computer vision Computational modeling Superresolution computer architecture Benchmark testing Robustness pattern recognition

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LSTM Based Character recognition for Text Extraction in Real-Time Natural Scenes Images 2

LSTM Based Character Recognition for Text Extraction in Real...

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2nd ieee International conference on Data Science and Network Security, ICDSNS 2024

作者： Kant Soni, Vishnu Chouksey, Praveen Tandan, S.R. Pimpalkar, Amit Miri, Rohit Nema, Neetesh Kumar Dr. C. V. Raman University Department of Computer Science and Engineering C.G. Bilaspur India Government R. V. R. S. Kanya Mahavidyalaya Department of Computer Science C.G. Kawardha India Nagpur India Utd Csvtu Department of Computer Science and Engineering C.G. Bhilai India

ISBN: (纸本)9798350373110

Extracting text from complex real-world images poses a significant challenge in computer vision due to cluttered backgrounds, diverse fonts, and varying orientations. Traditional methods struggle with accuracy in such scenarios. This research introduces a novel text extraction model, Deep Convolutional Neural Networks (DCNNs), for precise character recognition. By combining Optical Character recognition (OCR) with Long Short-Term Memory (LSTM) networks, the model effectively handles sequential data and enhances text extraction performance. By integrating OCR with LSTM, the proposed approach aims to overcome the limitations of traditional methods and enhance text extraction performance in challenging scenarios. Evaluation of ICDAR datasets yielded a high average confidence score of 95.50%, highlighting the model's accuracy in challenging text extraction tasks. Real-time testing in Bilaspur, India, demonstrated the model's robustness and adaptability, surpassing human annotations. These results highlight the potential of deep learning, particularly the OCR-LSTM fusion, for accurate text extraction in diverse natural scene images. © 2024 ieee.

关键词： Convolutional neural networks

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Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times

Digital Gimbal: End-to-end Deep Image Stabilization with Lea...

引用

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

作者： Dahary, Omer Jacoby, Matan Bronstein, Alex M. Technion Israel Inst Technol Haifa Israel

ISBN: (纸本)9781665445092

Mechanical image stabilization using actuated gimbals enables capturing long-exposure shots without suffering from blur due to camera motion. These devices, however, are often physically cumbersome and expensive, limiting their widespread use. In this work, we propose to digitally emulate a mechanically stabilized system from the input of a fast unstabilized camera. To exploit the trade-off between motion blur at long exposures and low SNR at short exposures, we train a CNN that estimates a sharp high-SNR image by aggregating a burst of noisy short-exposure frames, related by unknown motion. We further suggest learning the burst's exposure times in an end-to-end manner, thus balancing the noise and blur across the frames. We demonstrate this method's advantage over the traditional approach of deblurring a single image or denoising a fixed-exposure burst on both synthetic and real data.

关键词： computer vision Limiting Noise reduction Cameras pattern recognition Noise measurement Image reconstruction

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Automatic Bird Species recognition using Audio and Image Data: A Short Review 1

Automatic Bird Species Recognition using Audio and Image Dat...

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1st ieee International conference on Contemporary Computing and Communications, InC4 2023

作者： Kumar, Samparthi Kondaveeti, Hari Kishan Simhadri, Chinna Gopi Yasaswini Reddy, M. VIT-AP University School of Computer Science Engineering Andhra Pradesh Amaravati India

ISBN: (纸本)9798350335774

Studies related to bird species identification, movements, and behavior are important for protecting the environment and measuring biodiversity, especially for ornithological research and conservation. Researchers have conducted extensive research on automated bird recognition using images, videos, and audio recordings for biological studies, environmental monitoring, and bird detection and localization. Various approaches have been developed using machine learning, computer vision, and deep learning models. This review examines the current state of automatic bird recognition methods, including sources of data, models, and performance, as well as their limitations, research gaps, and future directions. © 2023 ieee.

关键词： Biodiversity

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Long-Tailed Multi-Label Visual recognition by Collaborative Training on Uniform and Re-balanced Samplings

Long-Tailed Multi-Label Visual Recognition by Collaborative ...

引用

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

作者： Guo, Hao Wang, Song Univ South Carolina Columbia SC 29201 USA

ISBN: (纸本)9781665445092

Long-tailed data distribution is common in many multi-label visual recognition tasks and the direct use of these data for training usually leads to relatively low performance on tail classes. While re-balanced data sampling can improve the performance on tail classes, it may also hurt the performance on head classes in training due to label co-occurrence. In this paper, we propose a new approach to train on both uniform and re-balanced samplings in a collaborative way, resulting in performance improvement on both head and tail classes. More specifically, we design a visual recognition network with two branches: one takes the uniform sampling as input while the other takes the rebalanced sampling as the input. For each branch, we conduct visual recognition using a binary-cross-entropy-based classification loss with learnable logit compensation. We further define a new cross-branch loss to enforce the consistency when the same input image goes through the two branches. We conduct extensive experiments on VOC-LT and COCO-LT datasets. The results show that the proposed method significantly outperforms previous state-of-the-art methods on long-tailed multi-label visual recognition.

关键词： Training Visualization computer vision Head Collaboration Collaborative work pattern recognition

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Advancements and Challenges in Low-Light Object Detection 2

Advancements and Challenges in Low-Light Object Detection

引用

2nd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Shrivastav, Pooja CMR Institute of Technology Bengaluru India

ISBN: (纸本)9798350327533

The study of computer vision has been drawing tremendous attention to low light object detection as the need for reliable vision systems that can function in difficult lighting circumstances is growing. Low levels of light pose a significant challenge to computer vision systems, particularly in the domain of object detection. While some efforts have been made to improve lighting, others have been concentrated on lowering noise. This research offers a thorough examination of both conventional and deep learning techniques used in low light object recognition, a crucial area of computer vision with important applications in everyday life. First, the basic ideas and techniques of classical algorithms, is highlighted. The discussion then turns to deep learning techniques, emphasizing current developments and their increasing prominence in the industry. A significant portion of the study examines current related works and carefully weighs the advantages and disadvantages of each. This comparison highlights the advancements in algorithmic sophistication while also illuminating the inherent trade-offs associated with various strategies. Typical obstacles including fluctuating illumination levels, noise interference, and dynamic range constraints are carefully investigated to comprehend how they affect algorithms. The study also describes different performance metrics that are used to assess object detection algorithms in low light. Analysis results are summarized and future research avenues are suggested in an effort to close the gaps in the state-of-The-Art approaches and improve the performance of low-light object detection algorithms. © 2024 ieee.

关键词： Compu ter vision Deep learning low-light object detection Machine l earning

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VISUALVOICE: Audio-Visual Speech Separation with Cross-Modal Consistency

VISUALVOICE: Audio-Visual Speech Separation with Cross-Modal...

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

作者： Gao, Ruohan Grauman, Kristen Univ Texas Austin Austin TX 78712 USA Stanford Univ Stanford CA 94305 USA Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

We introduce a new approach for audio-visual speech separation. Given a video, the goal is to extract the speech associated with a face in spite of simultaneous background sounds and/or other human speakers. Whereas existing methods focus on learning the alignment between the speaker's lip movements and the sounds they generate, we propose to leverage the speaker's face appearance as an additional prior to isolate the corresponding vocal qualities they are likely to produce. Our approach jointly learns audio-visual speech separation and cross-modal speaker embeddings from unlabeled video. It yields state-of-the-art results on five benchmark datasets for audiovisual speech separation and enhancement, and generalizes well to challenging real-world videos of diverse scenarios.

关键词： Location awareness computer vision Face recognition Lips Computational modeling Speech recognition Speech enhancement

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