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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

共 6538 条记录，以下是191-200 订阅

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Variational Autoencoders for Generating Hyperspectral Imaging Honey Adulteration Data

Variational Autoencoders for Generating Hyperspectral Imagin...

引用

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

作者： Phillips, Tessa Abdulla, Waleed Univ Auckland Auckland New Zealand

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Honey fraud and adulteration are an increasing concern globally. Hyperspectral imaging and machine learning can detect adulterated honey within a known set of honey, where we have captured data at different sugar concentrations. Previous work in this area has used a minimal number of honey types, as sample preparation and data capture is a time-consuming process. This paper develops a new approach using variational autoencoders (VAEs) for generating adulterated honey data for unseen honey types. The results show that the binary adulteration detector can achieve on average 81.3% accuracy on unseen honey types by adding the generated data to the existing training data. Without including the generated data while training, the classifier can only achieve 44% on unseen honey types.

关键词： Training computer vision conferences Training data Machine learning Detectors pattern recognition

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User-Guided Variable Rate Learned Image Compression

User-Guided Variable Rate Learned Image Compression

引用

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

作者： Gupta, Rushil Suryateja, B., V Kapoor, Nikhil Jaiswal, Rajat Nangi, Sharmila Kulkarni, Kuldeep Adobe Res Bengaluru India Indian Inst Technol Delhi Delhi India Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a learning-based image compression method that achieves any arbitrary input bitrate via user-guided bit allocation to preferred regions. We verify our hypothesis of incorporating user guidance for bitrate control by experimenting with alternatives that do not have any guidance. We conduct extensive evaluation on CelebA-HQ and CityScapes dataset using standard quantitative metrics and human studies showing that our single model for multiple bitrates achieves similar or better performance as compared to previous learned image compression methods that require re-training for each new bitrate.

关键词： Measurement computer vision Image coding conferences Computational modeling Bit rate pattern recognition

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NTIRE 2024 Image Shadow Removal Challenge Report

NTIRE 2024 Image Shadow Removal Challenge Report

引用

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

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Wu, Zongwei Zhou, Zhuyun Chen, Cailian Zhou, Han Timofte, Radu Dong, Wei Tian, Yuqiong Chen, Jun Lul, Xin Zhu, Yurui Wang, Xi Li, Dong Xiao, Jie Zhang, Yunpeng Fu, Xueyang Zha, Zheng-Jun Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Wei, Yanyan Xiaol, Jie Ful, Xueyang Zhal, Zheng-Jun Lu, Xin Zhao, Zhihao Sun, Long Yang, Tingting Pan, Jinshan Tang, Jinhui Dong, Jiangxin Benjdira, Bilel Nassif, Mohammed Koubaa, Anis Elhayek, Ahmed Ali, Anas M. Tokoro, Kyotaro Kawai, Kento Yokoyama, Kaname Seno, Takuya Kondo, Yuki Ukita, Norimichi Li, Chenghua Yang, Bo Wu, Zhiqi Chen, Gao Yu, Yihan Chen, Sixiang Mane, Kai Ye, Tian Zou, Wenbin Lin, Yunlong Xing, Zhaohu Bai, Jinbin Chai, Wenhao Zhu, Lei Maheshwari, Ritik Verma, Rakshank Tekchandanil, Rahul Hambarde, Praful Tazil, Satya Narayan Vipparthi, Santosh Kumar Murala, Subrahmanyam Lee, Jaeho Kim, Seongwan Sharif, S. M. A. Khujaev, Nodirkhuja Tsoy, Roman Gao, Fan Yan, Weidan Shao, Wenze Zhang, Dengyin Chen, Bin Zhang, Siqi Qian, Yanxin Chen, Yuanbin Zhou, Yuanbo Tong, Tong Wei, Rongfeng Sun, Ruiqi Liu, Yue Akalwadi, Nikhil Joshi, Amogh Malagi, Sampada Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Dukre, Adinath Deshmukh, Vivek Phutke, Shruti S. Kulkarni, Ashutosh Gonde, Anil Karthik, Arun K. Manasa, N. Priyal, Shri Hari Hao, Wei Yan, Xingzhuo Fu, Minghan Univ Wurzburg Comp Vis Lab IFI & CAIDAS Wurzburg Germany Shanghai Jiao Tong Univ Shanghai Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada Univ Sci & Technol China Hefei Peoples R China Hefei Univ Technol Hefei Peoples R China Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China Prince Sultan Univ Robot & Internet Things Lab Riyadh 12435 Saudi Arabia Prince Muqrin Univ Artificial Intelligence Dept Medinah 41311 Saudi Arabia Toyota Technol Inst Intelligent Informat Media Lab Nagoya Japan Nanjing Artificial Intelligence Res IA AiRiA Nanjing Peoples R China Nanjing Normal Univ High Sch Jiangning Campus Nanjing Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China South China Univ Technol Guangzhou Peoples R China Xiamen Univ Xiamen Peoples R China Natl Univ Singapore Singapore Singapore Univ Washington Seattle WA 98195 USA GEC Ajmer Kiranipura India CVPR Lab IIT Ropar Rupnagar India SCSS Trinity Coll Dublin Dublin Ireland Opt AI Seoul South Korea Nanjing Univ Posts & Telecommun Nanjing Peoples R China Fuzhou Univ Fuzhou Peoples R China Univ Hong Kong Logist & Supply Chain MultiTech R&D Ctr Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India KLE Technol Univ Sch Elect & Commun Engn Hubballi Karnataka India KLE Technol Univ Sch Comp Sci & Engn Hubballi Karnataka India Univ Teknol Malaysia Malaysia Japan Int Inst Technol MMT Kuala Lumpur Malaysia Univ Teknol Malaysia Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur Malaysia Shri Guru Gobind Singhji Inst Engn & Technol Nanded India Indian Inst Technol Ropar Comp Vis & Pattern Recognit Lab Rupnagar India Trinity Coll Dublin Sch Comp Sci & Stat CVPR Lab Dublin Ireland Shiv Nadar Univ Sch Engn Chennai Tamil Nadu India Fortinet Inc Sunnyvale CA USA Bosch Investment Ltd Shanghai Peoples R China Univ Saskatchewan Saskatoon

ISBN: (纸本)9798350365474

This work reviews the results of the NTIRE 2024 Challenge on Shadow Removal. Building on the last year edition, the current challenge was organized in two tracks, with a track focused on increased fidelity reconstruction, and a separate ranking for high performing perceptual quality solutions. Track 1 (fidelity) had 214 registered participants, with 17 teams submitting in the final phase, while Track 2 (perceptual) registered 185 participants, resulting in 18 final phase submissions. Both tracks were based on data from the WSRD dataset, simulating interactions between self-shadows and cast shadows, with a large variety of represented objects, textures, and materials. Improved image alignment enabled increased fidelity reconstruction, with restored frames mostly indistinguishable from the references images for top performing solutions.

关键词： lighting removal restoration shadow

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MV-TAL: Mulit-view Temporal Action Localization in Naturalistic Driving

MV-TAL: Mulit-view Temporal Action Localization in Naturalis...

引用

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

作者： Li, Wei Chen, Shimin Gu, Jianyang Wang, Ning Chen, Chen Guo, Yandong OPPO Res Inst Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China East China Univ Sci & Technol Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human risky behavior in driving is an important visual recognition problem. In this paper, we propose a multi-view temporal action localization system based on the grayscale video to achieve action recognition in naturalistic driving. Specifically, we adopted SwinTransformer as feature extractor, and a single framework to detect boundary and class at the same time. Also, we improve multiple loss function for explicit constraints of embedded feature distributions. Our proposed framework achieves the overall F1 -score of 0.3154 on A2 dataset.

关键词： Location awareness Measurement Visualization computer vision conferences Aggregates Gray-scale

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SMM-Conv: Scalar Matrix Multiplication with Zero Packing for Accelerated Convolution

SMM-Conv: Scalar Matrix Multiplication with Zero Packing for...

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

作者： Ofir, Amir Ben-Artzi, Gil Ariel Univ Ariel Israel

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a novel approach for accelerating convolutions during inference for CPU-based architectures. The most common method of computation involves packing the image into the columns of a matrix (im2col) and performing general matrix multiplication (GEMM) with a matrix of weights. This results in two main drawbacks: (a) im2col requires a large memory buffer and can experience inefficient memory access, and (b) while GEMM is highly optimized for scientific matrices multiplications, it is not well suited for convolutions. We propose an approach that takes advantage of scalar-matrix multiplication and reduces memory overhead. Our experiments with commonly used network architectures demonstrate a significant speedup compared to existing indirect methods.

关键词： Deep learning computer vision conferences Memory management Network architecture pattern recognition Kernel

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Towards Detailed Characteristic-Preserving Virtual Try-On

Towards Detailed Characteristic-Preserving Virtual Try-On

引用

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

作者： Lee, Sangho Lee, Seoyoung Lee, Joonseok Seoul Natl Univ Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

While virtual try-on has rapidly progressed recently, existing virtual try-on methods still struggle to faithfully represent various details of the clothes when worn. In this paper, we propose a simple yet effective method to better preserve details of the clothing and person by introducing an additional fitting step after geometric warping. This minimal modification enables disentangling representations of the clothing from the wearer, hence we are able to preserve the wearer-agnostic structure and details of the clothing, to fit a garment naturally to a variety of poses and body shapes. Moreover, we propose a novel evaluation framework applicable to any metric, to better reflect the semantics of clothes fitting. From extensive experiments, we empirically verify that the proposed method not only learns to disentangle clothing from the wearer, but also preserves details of the clothing on the try-on results.

关键词： Measurement computer vision Shape conferences Clothing Fitting Semantics

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PaintInStyle: One-Shot Discovery of Interpretable Directions by Painting

PaintInStyle: One-Shot Discovery of Interpretable Directions...

引用

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

作者： Doner, Berkay Balcioglu, Elif Sema Barin, Merve Rabia Kocasari, Umut Tiftikci, Mert Yanardag, Pinar Bogazici Univ Istanbul Turkey

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The search for interpretable directions in latent spaces of pre-trained Generative Adversarial Networks (GANs) has become a topic of interest. These directions can be utilized to perform semantic manipulations on the GAN generated images. The discovery of such directions is performed either in a supervised way, which requires manual annotation or pre-trained classifiers, or in an unsupervised way, which requires the user to interpret what these directions represent. In this work, we propose a framework that finds a specific manipulation direction using only a single simple sketch drawn on an image. Our method finds directions consisting of channels in the style space of the StyleGAN2 architecture responsible for the desired edits and performs image manipulations comparable with state-of-the-art methods.

关键词： computer vision Annotations conferences Semantics Manuals computer architecture Generative adversarial networks

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Area Under the ROC Curve Maximization for Metric Learning

Area Under the ROC Curve Maximization for Metric Learning

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

作者： Gajic, Bojana Amato, Ariel Baldrich, Ramon van de Weijer, Joost Gatta, Carlo Vintra Inc Barcelona Spain Comp Vis Ctr Barcelona Spain

ISBN: (纸本)9781665487399

Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification.

关键词： Training computer vision conferences Area measurement Benchmark testing pattern recognition

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Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity

Proposal-free Lidar Panoptic Segmentation with Pillar-level ...

引用

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

作者： Chen, Qi Vora, Sourabh Johns Hopkins Univ Baltimore MD 21218 USA Motional Boston MA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a simple yet effective proposal-free architecture for lidar panoptic segmentation. We jointly optimize both semantic segmentation and class-agnostic instance classification in a single network using a pilla-rbased bird's-eye view representation. The instance classification head learns pairwise affinity between pillars to determine whether the pillars belong to the same instance or not. We further propose a local clustering algorithm to propagate instance ids by merging semantic segmentation and affinity predictions. Our experiments on nuScenes dataset show that our approach outperforms previous proposal-free methods and is comparable to proposal-based methods which requires extra annotation from object detection.

关键词： computer vision Laser radar conferences Semantics Merging Clustering algorithms Object detection

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SCVRL: Shuffled Contrastive Video Representation Learning

SCVRL: Shuffled Contrastive Video Representation Learning

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

作者： Dorkenwald, Michael Xiao, Fanyi Brattoli, Biagio Tighe, Joseph Modolo, Davide Heidelberg Univ Heidelberg Germany AWS AI Labs Palo Alto CA USA AWS Palo Alto CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos. Differently from previous contrast learning based methods that mostly focus on learning visual semantics (e.g., CVRL), SCVRL is capable of learning both semantic and motion patterns. For that, we reformulate the popular shuffling pretext task within a modern contrastive learning paradigm. We show that our transformer-based network has a natural capacity to learn motion in self-supervised settings and achieves strong performance, outperforming CVRL on four benchmarks.

关键词： Representation learning Visualization Semantics Self-supervised learning Benchmark testing Transformers pattern recognition

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