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

共 8962 条记录，以下是341-350 订阅

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Embedded Contours Extraction for High-Speed Scene Dynamics Based on a Neuromorphic Temporal Contrast vision Sensor

Embedded Contours Extraction for High-Speed Scene Dynamics B...

引用

ieee conference on computer vision and pattern recognition

作者： Belbachir, A. N. Hofstaetter, M. Milosevic, N. Schoen, P. Austrian Res Ctr GmbH ARC Smart Syst Div A-1220 Vienna Austria

ISBN: (纸本)9781424423392

The paper presents a compact vision system for efficient contours extraction in high-speed applications. By exploiting the ultra high temporal resolution and the sparse representation of the sensors data in reacting to scene dynamics, the system fosters efficient embedded computer vision for ultra high-speed applications. The results reported in this paper show the sensor output quality for a wide range of object velocity (5-40 m/s), and demonstrate the object data volume independence from the velocity as well as the steadiness of the object quality. The influence of object velocity on high-performance embedded computer vision is also discussed.

关键词： Extraction

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Contrastive Domain Adaptation

Contrastive Domain Adaptation

引用

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

作者： Thota, Mamatha Leontidis, Georgios Univ Lincoln Sch Comp Sci Lincoln LN6 7TS England Univ Aberdeen Dept Comp Sci Aberdeen AB24 3UE Scotland

ISBN: (纸本)9781665448994

Recently, contrastive self-supervised learning has become a key component for learning visual representations across many computer vision tasks and benchmarks. However, contrastive learning in the context of domain adaptation remains largely underexplored. In this paper, we propose to extend contrastive learning to a new domain adaptation setting, a particular situation occurring where the similarity is learned and deployed on samples following different probability distributions without access to labels. Contrastive learning learns by comparing and contrasting positive and negative pairs of samples in an unsupervised setting without access to source and target labels. We have developed a variation of a recently proposed contrastive learning framework that helps tackle the domain adaptation problem, further identifying and removing possible negatives similar to the anchor to mitigate the effects of false negatives. Extensive experiments demonstrate that the proposed method adapts well, and improves the performance on the downstream domain adaptation task.

关键词： Training computer vision Adaptation models Visualization Machine learning algorithms Pipelines Machine learning

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Kernel ELM and CNN based Facial Age Estimation 29

Kernel ELM and CNN based Facial Age Estimation

引用

29th ieee conference on computer vision and pattern recognition (CVPR)

作者： Gurpinar, Furkan Kaya, Heysem Dibeklioglu, Hamdi Salah, Albert Ali Bogazici Univ Program Computat Sci & Engn Istanbul Turkey Namik Kemal Univ Dept Comp Engn Tekirdag Turkey Delft Univ Technol Pattern Recognit & Bioinformat Grp Delft Netherlands Bogazici Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781509014378

e propose a two-level system for apparent age estimation from facial images. Our system first classifies samples into overlapping age groups. Within each group, the apparent age is estimated with local regressors, whose outputs are then fused for the final estimate. We use a deformable parts model based face detector, and features from a pre-trained deep convolutional network. Kernel extreme learning machines are used for classification. We evaluate our system on the ChaLearn Looking at People 2016 - Apparent Age Estimation challenge dataset, and report 0.3740 normal score on the sequestered test set.

关键词： Learning systems

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Perceptual Loss for Robust Unsupervised Homography Estimation

Perceptual Loss for Robust Unsupervised Homography Estimatio...

引用

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

作者： Koguciuk, Daniel Arani, Elahe Zonooz, Bahram NavInfo Europe Adv Res Lab Eindhoven Netherlands

ISBN: (纸本)9781665448994

Homography estimation is often an indispensable step in many computer vision tasks. The existing approaches, however, are not robust to illumination and/or larger viewpoint changes. In this paper, we propose bidirectional implicit Homography Estimation (biHomE) loss for unsupervised homography estimation. biHomE minimizes the distance in the feature space between the warped image from the source viewpoint and the corresponding image from the target viewpoint. Since we use a fixed pre-trained feature extractor and the only learnable component of our framework is the homography network, we effectively decouple the homography estimation from representation learning. We use an additional photometric distortion step in the synthetic COCO dataset generation to better represent the illumination variation of the real-world scenarios. We show that biHomE achieves state-of-the-art performance on synthetic COCO dataset, which is also comparable or better compared to supervised approaches. Furthermore, the empirical results demonstrate the robustness of our approach to illumination variation compared to existing methods.

关键词： Degradation computer vision conferences Estimation Lighting Feature extraction Distortion

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A vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees

A Vision-based System for Traffic Anomaly Detection using De...

引用

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

作者： Aboah, Armstrong Shoman, Maged Mandal, Vishal Davami, Sayedomidreza Adu-Gyamfi, Yaw Sharma, Anuj Univ Missouri Columbia Dept Civil & Environm Engn Columbia MO 65211 USA Iowa State Univ Dept Civil & Environm Engn Ames IA USA

ISBN: (纸本)9781665448994

Any intelligent traffic monitoring system must be able to detect anomalies such as traffic accidents in real time. In this paper, we propose a Decision-Tree enabled approach powered by deep learning for extracting anomalies from traffic cameras while accurately estimating the start and end times of the anomalous event. Our approach included creating a detection model, followed by anomaly detection and analysis. YOLOv5 served as the foundation for our detection model. The anomaly detection and analysis step entail traffic scene background estimation, road mask extraction, and adaptive thresholding. Candidate anomalies were passed through a decision tree to detect and analyze final anomalies. The proposed approach yielded an F1 score of 0.8571, and an S4 score of 0.5686, per the experimental validation.

关键词： Deep learning Adaptation models Roads conferences Estimation Real-time systems pattern recognition

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The Role of Shape for Domain Generalization on Sparsely-Textured Images

The Role of Shape for Domain Generalization on Sparsely-Text...

引用

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

作者： Nazari, Narges Honarvar Kovashka, Adriana Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

State-of-the-art object recognition methods do not generalize well to unseen domains. Work in domain generalization has attempted to bridge domains by increasing feature compatibility, but has focused on standard, appearance-based representations. We show the potential of shape-based representations to increase domain robustness. We compare two types of shape-based representations: one trains a convolutional network over edge features, and another computes a soft, dense medial axis transform. We show the complementary strengths of these representations for different types of domains, and the effect of the amount of texture that is preserved. We show that our shape-based techniques better leverage data augmentations for domain generalization, and are more effective at texture bias mitigation than shape-inducing augmentations. Finally, we show that when the convolutional network in state-of-the-art domain generalization methods is replaced with one that explicitly captures shape, we obtain improved results.

关键词： Bridges computer vision Shape conferences Transforms Robustness pattern recognition

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NTIRE 2022 Challenge on Learning the Super-Resolution Space

NTIRE 2022 Challenge on Learning the Super-Resolution Space

引用

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

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Kim, Kang-wook Kim, Younggeun Lee, Jae-young Li, Zechao Pan, Jinshan Shim, Dongseok Song, Ki-Ung Tang, Jinhui Wang, Cong Zhao, Zhihao Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reviews the NTIRE 2022 challenge on learning the super-Resolution space. This challenge aims to raise awareness that the super-resolution problem is ill-posed. Since many high-resolution images map to the same low-resolution image, we asked the participants to create methods that sample diverse super-resolution from the space of possible high-resolution images given a low-resolution image. For evaluation, we use the same protocol as introduced in the last year's super-resolution space challenge of NTIRE 2021. We compare the submissions of the participating teams and relate them to the approaches from last year. This challenge contains two tracks: 4x and 8x scale factor. In total, 3 teams competed in the final testing phase.

关键词： computer vision Protocols conferences Superresolution pattern recognition Testing

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Deep Image Compression with Latent Optimization and Piece-wise Quantization Approximation

Deep Image Compression with Latent Optimization and Piece-wi...

引用

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

作者： Wu, Yuyang Qi, Zhiyang Zheng, Huiming Tao, Lvfang Gao, Wei Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Benefit from its capability of learning high-dimensional compact representation from raw data, the auto-encoders are widely used in various tasks of data compression. In particular, for deep image compression, auto-encoders generally take the responsibility of mapping original images to the latent representation to be coded. In this paper, we propose a new framework for deep image compression by devising a loss function for latent optimization, and adopting the differentiable approximation of quantization. In our experiments, both subjective and objective results can confirm the effectiveness of our contributions.

关键词： computer vision Image coding Quantization (signal) conferences Data compression pattern recognition Task analysis

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Experiments on an RGB-D Wearable vision System for Egocentric Activity recognition

Experiments on an RGB-D Wearable Vision System for Egocentri...

引用

27th ieee conference on computer vision and pattern recognition (CVPR)

作者： Moghimi, Mohammad Azagra, Pablo Montesano, Luis Murillo, Ana C. Belongie, Serge Univ Calif San Diego La Jolla CA 92093 USA Univ Zaragoza DIIS I3A Zaragoza Spain Cornell Tech New York NY USA

ISBN: (纸本)9781479943098

This work describes and explores novel steps towards activity recognition from an egocentric point of view. Activity recognition is a broadly studied topic in computer vision, but the unique characteristics of wearable vision systems present new challenges and opportunities. We evaluate a challenging new publicly available dataset that includes trajectories of different users across two indoor environments performing a set of more than 20 different activities. The visual features studied include compact and global image descriptors, including GIST and a novel skin segmentation based histogram signature, and state-of-the art image representations for recognition, including Bag of SIFT words and Convolutional Neural Network (CNN) based features. Our experiments show that simple and compact features provide reasonable accuracy to obtain basic activity information (in our case, manipulation vs. non-manipulation). However, for finer grained categories CNN-based features provide the most promising results. Future steps include integration of depth information with these features and temporal consistency into the pipeline.

关键词： computer vision

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An Effective Temporal Localization Method with Multi-View 3D Action recognition for Untrimmed Naturalistic Driving Videos

An Effective Temporal Localization Method with Multi-View 3D...

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

作者： Manh Tung Tran Minh Quan Vu Ngoc Duong Hoang Khac-Hoai Nam Bui Viettel Grp Viettel Cyperspace Ctr Hanoi Vietnam

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Naturalistic driving studies with computer vision techniques have become an emergent research issue. The objective is to classify the distracted behavior actions by drivers. Specifically, this issue is regarded as temporal action localization (TAL) of untrimmed videos, which is a challenging task in the research field of video analysis. Particularly, TAL remains as one of the most challenging unsolved problems in computer vision that requires not only the recognition of action but the localization of the start and end times of each action. Most state-of-the-art approaches adopt complex architectures, which are expensive training and inefficient inference time. In this study, we propose a new framework for untrimmed naturalistic driving videos by utilizing the results from 3D action recognition with video clip classification for short temporal and spatial correlation. Then, simple post-processing based on data-driven is presented for long temporal correlation in untrimmed videos. The proposed method is evaluated on the AI City Challenge 2022 dataset for Naturalistic Driving Action recognition. Accordingly, our method achieves the top 1 on the public leaderboard of the challenge.

关键词： Location awareness Training computer vision Solid modeling Correlation Urban areas X3D

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