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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

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Video Analytics for Detecting Motorcyclist Helmet Rule Violations

Video Analytics for Detecting Motorcyclist Helmet Rule Viola...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Chun-Ming Tsai Jun-Wei Hsieh Ming-Ching Chang Guan-Lin He Ping-Yang Chen Wei-Tsung Chang Yi-Kuan Hsieh Department of Computer Science University of Taipei Taipei Taiwan College of AI and Green Energy National Yang Ming Chiao Tung University Tainan Taiwan Department of Computer Science University at Albany State University of New York NY USA

The use of helmets is essential for motorcyclists' safety, but non-compliance with helmet rules remains a common issue. In this study, we extend the frontier of AI video analytic technologies for detecting violations of helmet rules among motorcyclists. Our method can handle highly challenging conditions for traditional methods, including occlusions, fast vehicle movement, shadows, large viewing angles, poor illumination and weather conditions. We adopt the widely used YOLOv7 object detector and develop a first baseline using YOLOv7-E6E. We further develop two improved versions, namely YOLOv7-CBAM and YOLOv7-SimAM that better address the challenges. Experiments are performed on the 2023 AI City Challenge Track 5 contest benchmark. Evaluation on the 100 test videos of the contest demonstrates the effectiveness of our approach. The baseline YOLOv7-E6E model trained with image size 1920 achieves 0.6112 mAP. The YOLOv7-CBAM achieves 0.6389 mAP, and YOLOv7-SimAM achieves 0.6422 mAP, where both are trained with image size 1280. These models rank sixth, fifth, and fourth on the public leaderboard, respectively, which outperforms over 36 global participating teams. The code for our models is available at: https://***/cmtsai2023/AICITY2023_Track5_DVHRM.

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E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation

E2EC: An End-to-End Contour-based Method for High-Quality Hi...

引用

2022 ieee/cvf conference on computer vision and pattern recognition, CVPR 2022

作者： Zhang, Tao Wei, Shiqing Ji, Shunping Wuhan University Wuhan China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Contour-based instance segmentation methods have developed rapidly recently but feature rough and hand-crafted front-end contour initialization, which restricts the model performance, and an empirical and fixed backend predicted-label vertex pairing, which contributes to the learning difficulty. In this paper, we introduce a novel contour-based method, named E2EC, for high-quality instance segmentation. Firstly, E2EC applies a novel learnable contour initialization architecture instead of hand-crafted contour initialization. This consists of a contour initialization module for constructing more explicit learning goals and a global contour deformation module for taking advantage of all of the vertices' features better. Secondly, we propose a novel label sampling scheme, named multi-direction alignment, to reduce the learning difficulty. Thirdly, to improve the quality of the boundary details, we dynamically match the most appropriate predicted-ground truth vertex pairs and propose the corresponding loss function named dynamic matching loss. The experiments showed that E2EC can achieve a state-of-the-art performance on the KITTI INStance (KINS) dataset, the Semantic Boundaries Dataset (SBD), the Cityscapes and the COCO dataset. E2EC is also efficient for use in real-time applications, with an inference speed of 36 fps for $512\times 512$ images on an NVIDIA A6000 GPU. Code will be released at https://***/zhang-tao-whu/e2ec. © 2022 ieee.

关键词： Image segmentation Shape Semantics Graphics processing units computer architecture Detectors Predictive models

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Semi-Stereo: A Universal Stereo Matching Framework for Imperfect Data via Semi-supervised Learning

Semi-Stereo: A Universal Stereo Matching Framework for Imper...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xin Yue Zongqing Lu Xiangru Lin Wenjia Ren Zhijing Shao Haonan Hu Yu Zhang Qingmin Liao Tsinghua University Prometheus Vision Technology Co. Ltd.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Data matters in deep-learning-based binocular stereo matching. Obtaining a perfect dataset for stereo matching is hard and thus imperfect data is common in existing benchmark datasets, such as KITTI, ETH3D and Middlebury. The imperfectness typically has two forms: sparse-labeled data or even unlabeled data. Current stereo matching networks ignore the supervision from these imperfect data itself, even the semi-supervised networks often suffer from confirmation bias in the predictions. Besides, current methods lack a unified solution to utilize the supervision signal from those imperfect data. To mitigate this research gap, we propose Semi-Stereo, the first unified stereo matching framework empowered by the teacher-student paradigm where the teacher and the student networks are trained in a mutual-beneficial manner. To explore the rich knowledge in imperfect data, we propose a consistency regularization module with weak-strong augmentation strategies. Further, in order for the teacher to provide more reliable pseudo labels, we design a confidence module, powered by left-right consistency (LRC) check and disparity distribution entropy (DDE). Extensive experiments demonstrate Semi-Stereo produces accurate and consistent predictions in untrained semantic regions and improves the performance of baseline networks in multiple tasks, including domain adaptation and domain generalization.

关键词： Measurement computer vision conferences computer network reliability Semantics Semisupervised learning Benchmark testing

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NTIRE 2024 Challenge on HR Depth from Images of Specular and Transparent Surfaces

NTIRE 2024 Challenge on HR Depth from Images of Specular and...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Pierluigi Zama Ramirez Fabio Tosi Luigi Di Stefano Radu Timofte Alex Costanzino Matteo Poggi Samuele Salti Stefano Mattoccia Yangyang Zhang Cailin Wu Zhuangda He Shuangshuang Yin Jiaxu Dong Yangchenxu Liu Hao Jiang Jun Shi Yong A Yixiang Jin Dingzhe Li Bingxin Ke Anton Obukhov Tinafu Wang Nando Metzger Shengyu Huang Konrad Schindler Yachuan Huang Jiaqi Li Junrui Zhang Yiran Wang Zihao Huang Tianqi Liu Zhiguo Cao Pengzhi Li Jui-Lin Wang Wenjie Zhu Hui Geng Yuxin Zhang Long Lan Kele Xu Tao Sun Qisheng Xu Sourav Saini Aashray Gupta Sahaj K. Mistry Aryan Shukla Vinit Jakhetiya Sunil Jaiswal Yuejin Sun Zhuofan Zheng Yi Ning Jen-Hao Cheng Hou-I Liu Hsiang-Wei Huang Cheng-Yen Yang Zhongyu Jiang Yi-Hao Peng Aishi Huang Jenq-Neng Hwang

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement (NTIRE) workshop at CVPR 2024. This challenge aims to advance the research on depth estimation, specifically to address two of the main open issues in the field: high-resolution and non-Lambertian surfaces. The challenge proposes two tracks on stereo and single-image depth estimation, attracting about 120 registered participants. In the final testing stage, 2 and 8 participating teams submitted their models and fact sheets for the two tracks.

关键词： computer vision conferences Computational modeling Estimation Market research pattern recognition Image restoration

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Unknown Sample Discovery for Source Free Open Set Domain Adaptation

Unknown Sample Discovery for Source Free Open Set Domain Ada...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Chowdhury Sadman Jahan Andreas Savakis Center for Imaging Science Rochester Institute of Technology Rochester NY USA Department of Computer Engineering Rochester Institute of Technology Rochester NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Open Set Domain Adaptation (OSDA) aims to adapt a model trained on a source domain to a target domain that undergoes distribution shift and contains samples from novel classes outside the source domain. Test-time or source-free OSDA (SF-OSDA) techniques eliminate the need to access source domain samples, but current SF-OSDA methods utilize only the known classes in the target domain for adaptation, and require access to the entire target domain even during inference after adaptation, to distinguish between known and unknown samples. In this paper, we introduce Unknown Sample Discovery (USD) as an SF-OSDA method that utilizes a temporally ensembled teacher model to conduct known-unknown target sample separation and adapts the student model to the target domain over all classes using co-training and temporal consistency between the teacher and the student. USD promotes Jensen-Shannon distance (JSD) as an effective measure for known-unknown sample separation. Our teacher-student framework significantly reduces error accumulation resulting from imperfect known-unknown sample separation, while curriculum guidance helps to reliably learn the distinction between target known and target unknown subspaces. USD appends the target model with an unknown class node, thus readily classifying a target sample into any of the known or unknown classes in subsequent post-adaptation inference stages. Empirical results show that USD is superior to existing SF-OSDA methods and is competitive with current OSDA models that require both source and target domains during adaptation.

关键词： Adaptation models computer vision conferences Computational modeling Data models pattern recognition Reliability

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A Single Residual Network with ESA Modules and Distillation

A Single Residual Network with ESA Modules and Distillation

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yucong Wang Minjie Cai College of Computer Science and Electronic Engineering Hunan University

Although there are many methods based on deep learning that have superior performance on single image super-resolution (SISR), it is difficult to run in real time on devices with limited computing power. Some recent studies have found that simply relying on reducing parameters or reducing the theoretical FLOPs of the model does not speed up the inference time of the network in a practical sense. Actual speed on the device is probably a better measure than FLOPs. In this work, we propose a new single residual network (SRN). On the one hand, we try to introduce and optimize an attention mechanism module to improve the performance of the network with a relatively small speed loss. On the other hand, we find that residuals in residual blocks do not have a positive impact on networks with adjusted ESA. Therefore, the residual of the network residual block is removed, which not only improves the speed of the network, but also improves the performance of the network. Finally, we reduced the number of channels and the number of residual blocks of the classic model EDSR, and removed the last convolution before the long residual. We set this tuned EDSR as the teacher model and our newly proposed SRN as the student model. Under the joint effect of the original loss and the distillation loss, the performance of the network can be improved without losing the inference time. Combining the above strategies, our proposed model runs much faster than similarly performing models. As an example, we built a Fast and Efficient Network (SRN) and its small version SRN-S, which run 30%-37% faster than the state-of-the-art EISR model: a paper champion RLFN. Furthermore, the shallow version of SRN-S achieves the second-shortest inference time as well as the second-smallest number of activations in the NTIRE2023 challenge. Code will be available at https://***/wnxbwyc/SRN.

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Advancing COVID-19 Detection in 3D CT Scans

Advancing COVID-19 Detection in 3D CT Scans

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Qingqiu Li Runtian Yuan Junlin Hou Jilan Xu Yuejie Zhang Rui Feng Hao Chen School of Academy for Engineering and Technology Fudan University China School of Computer Science Fudan University China Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model. Firstly, we analyze the characteristics of 3D CT scans and remove the non-lung parts, facilitating the model to focus on lesion-related areas and reducing computational cost. We use ResNeSt-50 as the strong feature extractor, exploring various pre-trained weights and fine-tuning methods. After a thorough comparison, we initialize our model with CMC v1 pre-trained weights which incorporate COVID-19-specific prior knowledge, and perform Visual Prompt Tuning to reduce the number of training parameters. The superiority of our model is demonstrated through extensive experiments, showing significant improvements in COVID-19 detection performance compared to the baseline model. Among 12 participating teams, our method ranked 4th in the 4th COVID-19 Competition Challenge I with an average Macro F1 Score of 94.24%.

关键词： COVID-19 Training Solid modeling Visualization Three-dimensional displays Computational modeling Computed tomography

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ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery

ConceptHash: Interpretable Fine-Grained Hashing via Concept ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Kam Woh Ng Xiatian Zhu Yi-Zhe Song Tao Xiang CVSSP University of Surrey iFlyTek-Surrey Joint Research Centre on Artificial Intelligence Surrey Institute for People-Centred Artificial Intelligence

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing fine-grained hashing methods typically lack code interpretability as they compute hash code bits holistically using both global and local features. To address this limitation, we propose ConceptHash, a novel method that achieves sub-code level interpretability. In ConceptHash, each sub-code corresponds to a human-understandable concept, such as an object part, and these concepts are automatically discovered without human annotations. Specifically, we leverage a vision Transformer architecture and introduce concept tokens as visual prompts, along with image patch tokens as model inputs. Each concept is then mapped to a specific sub-code at the model output, providing natural sub-code interpretability. To capture subtle visual differences among highly similar sub-categories (e.g., bird species), we incorporate language guidance to ensure that the learned hash codes are distinguishable within fine-grained object classes while maintaining semantic alignment. This approach allows us to develop hash codes that exhibit similarity within families of species while remaining distinct from species in other families. Extensive experiments on four fine-grained image retrieval benchmarks demonstrate that ConceptHash outperforms previous methods by a significant margin, offering unique sub-code interpretability as an additional benefit. Code at: https://***/kamwoh/concepthash.

关键词： Visualization computer vision Codes Semantics Image retrieval Manuals Inspection

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Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results

Wild Face Anti-Spoofing Challenge 2023: Benchmark and Result...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dong Wang Jia Guo Qiqi Shao Haochi He Zhian Chen Chuanbao Xiao Ajian Liu Sergio Escalera Hugo Jair Escalante Zhen Lei Jun Wan Jiankang Deng MoreDian InsightFace CASIA Computer Vision Center (UAB) University of Barcelona INAOE

Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which often leads to overfitting during training or saturation during testing. In terms of quantity, the number of spoof subjects is a critical determinant. Most datasets comprise fewer than 2,000 subjects. With regard to diversity, the majority of datasets consist of spoof samples collected in controlled environments using repetitive, mechanical processes. This data collection methodology results in homogenized samples and a dearth of scenario diversity. To address these shortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, a large-scale, diverse FAS dataset collected in unconstrained settings. Our dataset encompasses 853,729 images of 321,751 spoof subjects and 529,571 images of 148,169 live subjects, representing a substantial increase in quantity. Moreover, our dataset incorporates spoof data obtained from the internet, spanning a wide array of scenarios and various commercial sensors, including 17 presentation attacks (PAs) that encompass both 2D and 3D forms. This novel data collection strategy markedly enhances FAS data diversity. Leveraging the WFAS dataset and Protocol 1 (Known-Type), we host the Wild Face Anti-Spoofing Challenge at the CVPR2023 workshop. Additionally, we meticulously evaluate representative methods using Protocol 1 and Protocol 2 (Unknown-Type). Through an in-depth examination of the challenge outcomes and benchmark baselines, we provide insightful analyses and propose potential avenues for future research. The dataset is released under Insightface 1 .

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Understanding ReLU Network Robustness Through Test Set Certification Performance

Understanding ReLU Network Robustness Through Test Set Certi...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nicola Franco Jeanette Miriam Lorenz Karsten Roscher Stephan Günnemann Fraunhofer Institute for Cognitive Systems IKS Munich Germany Dept. of Computer Science & Munich Data Science Institute Technical Univ. of Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural networks can be vulnerable to small changes in input within their learning distribution, and this vulnerability increases for distributional shifts or input completely outside their training distribution. To ensure networks are used safely, robustness certificates offer formal assurances about the stability of their predictions in a pre-defined range around the input. However, the relationship between correctness and certified robustness remains unclear. In this work, we investigate the unexpected outcomes of verification methods applied to piecewise linear classifiers for clean, perturbed, in- and out-of-distribution samples. In our experiments focused on image classification, we observed that introducing a modest stability margin around the input sample leads to an important reduction in misclassified samples — approximately a 75% decrease — compared to the roughly 11% for samples that are correctly classified. This finding emphasizes the value of formal verification methods as an extra layer of safety, illustrating their effectiveness in enhancing accuracy for data that falls within the distribution. On the other hand, we provide a theoretical demonstration that formal verification methods robustly certify samples sufficiently far from the training distribution. These results are integrated with an experimental analysis and demonstrate their limitations compared to standard out-of-distribution detection methods.

关键词： Accuracy Perturbation methods Neural networks Reliability theory Robustness Stability analysis Safety

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