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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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MoCap-to-Visual Domain Adaptation for Efficient Human Mesh Estimation from 2D Keypoints

MoCap-to-Visual Domain Adaptation for Efficient Human Mesh E...

引用

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

作者： Bedirhan Uguz Ozhan Suat Batuhan Karagoz Emre Akbas Department of Computer Engineering Middle East Technical University Ankara Turkey METU ROMER Robotics Center Middle East Technical University Ankara Turkey

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents Key2Mesh, a model that takes a set of 2D human pose keypoints as input and estimates the corresponding body mesh. Since this process does not involve any visual (i.e. RGB image) data, the model can be trained on large-scale motion capture (MoCap) datasets, thereby overcoming the scarcity of image datasets with 3D labels. To enable the model’s application on RGB images, we first run an off-the-shelf 2D pose estimator to obtain the 2D keypoints, and then feed these 2D keypoints to Key2Mesh. To improve the performance of our model on RGB images, we apply an adversarial domain adaptation (DA) method to bridge the gap between the MoCap and visual domains. Crucially, our DA method does not require 3D labels for visual data, which enables adaptation to target sets without the need for costly labels. We evaluate Key2Mesh for the task of estimating 3D human meshes from 2D key-points, in the absence of RGB and mesh label pairs. Our results on widely used H3.6M and 3DPW datasets show that Key2Mesh sets the new state-of-the-art by outperforming other models in PA-MPJPE for both datasets, and in MPJPE and PVE for the 3DPW dataset. Thanks to our model’s simple architecture, it operates at least 12× faster than the prior state-of-the-art model, LGD [44]. Additional qualitative samples and code are available on the project website: https://***/.

关键词： Training Adaptation models Visualization Solid modeling Three-dimensional displays Computational modeling Training data

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LSQ+: Improving low-bit quantization through learnable offsets and better initialization

LSQ+: Improving low-bit quantization through learnable offse...

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

作者： Yash Bhalgat Jinwon Lee Markus Nagel Tijmen Blankevoort Nojun Kwak Qualcomm AI Research Qualcomm Technologies Inc. Qualcomm AI Research Qualcomm Technologies Netherlands B.V. Seoul National University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Unlike ReLU, newer activation functions (like Swish, H-swish, Mish) that are frequently employed in popular efficient architectures can also result in negative activation values, with skewed positive and negative ranges. Typical learnable quantization schemes [5], [7] assume unsigned quantization for activations and quantize all negative activations to zero which leads to significant loss in performance. Naively using signed quantization to accommodate these negative values requires an extra sign bit which is expensive for low-bit (2-, 3-, 4-bit) quantization. To solve this problem, we propose LSQ+, a natural extension of LSQ [7], wherein we introduce a general asymmetric quantization scheme with trainable scale and offset parameters that can learn to accommodate the negative activations. Gradient-based learnable quantization schemes also commonly suffer from high instability or variance in the final training performance, hence requiring a great deal of hyper-parameter tuning to reach a satisfactory performance. LSQ+ alleviates this problem by using an MSE-based initialization scheme for the quantization parameters. We show that this initialization leads to significantly lower variance in final performance across multiple training runs. Overall, LSQ+ shows state-of-the-art results for EfficientNet and MixNet and also significantly outperforms LSQ for low-bit quantization of neural nets with Swish activations (e.g.: 1.8% gain with W4A4 quantization and upto 5.6% gain with W2A2 quantization of EfficientNet-B0 on ImageNet dataset). To the best of our knowledge, ours is the first work to quantize such architectures to extremely low bit-widths.

关键词： Quantization (signal) Training Clamps Neural networks Artificial intelligence computer architecture Minimization

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AI City Challenge 2020 – computer vision for Smart Transportation Applications

AI City Challenge 2020 – Computer Vision for Smart Transpor...

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

作者： Ming-Ching Chang Chen-Kuo Chiang Chun-Ming Tsai Yun-Kai Chang Hsuan-Lun Chiang Yu-An Wang Shih-Ya Chang Yun-Lun Li Ming-Shuin Tsai Hung-Yu Tseng University at Albany – SUNY NY USA National Chung Cheng University Taiwan University of Taipei Taiwan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We present methods developed in our participation of the AI City 2020 Challenge (AIC20) and report evaluation results in this contest. With the blooming of AI computer vision techniques, vehicle detection, tracking, identification, and counting all have advanced significantly. However, whether these technologies are ready for real-world smart transportation usage is still a open question. The goal of this work is to apply and integrate state-of-the-art techniques for solving the challenge problems under a standardized setup and evaluation. We participated all 4 AIC20 challenge tracks (T1 to T4). In T1 challenge, we perform vehicle counting by associating deep features extracted from Mask-RCNN detections and tracklets, followed by vehicle movement zone matching. In T2 challenge, we perform vehicle type and color classification and then rank matching vehicles using a PGAM re-id network. In T3 challenge, we proposed a new Multi-Camera Tracking Network (MTCN) that takes single-camera vehicle tracking as input, and performs multi-camera tracklet fusion and linking, by jointly optimizing the matching of vehicle appearance and physical features. In T4 challenge, we adopt a leading method based on perspective detection and spatial-temporal matrix discriminating, and improve it with background modeling for traffic anomaly detection. We achieved top-6 and top-4 performance for T3 and T4 challenges respectively in the AIC20 general leaderboard.

关键词： Tracking Cameras Feature extraction Artificial intelligence Vehicle detection Trajectory Urban areas

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AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design

AttoNets: Compact and Efficient Deep Neural Networks for the...

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

作者： Alexander Wong Zhong Qiu Lin Brendan Chwyl Vision & Image Processing Research Group Waterloo AI Institute University of Waterloo DarwinAI Corp. Waterloo Canada

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

While deep neural networks have achieved state-of-the-art performance across a large number of complex tasks, it remains a big challenge to deploy such networks for practical, on-device edge scenarios such as on mobile devices, consumer devices, drones, and vehicles. There has been significant recent effort in designing small, low-footprint deep neural networks catered for low-power edge devices, with much of the focus on two extremes: hand-crafting via design principles or fully automated network architecture search. In this study, we take a deeper exploration into a human-machine collaborative design approach for creating highly efficient deep neural networks through a synergy between principled network design prototyping and machine-driven design exploration. The efficacy of human-machine collaborative design is demonstrated through the creation of AttoNets, a family of highly efficient deep neural networks for on-device edge deep learning. Each AttoNet possesses a human-specified network-level macro-architecture comprising of custom modules with unique machine-designed module-level macro-architecture and micro-architecture designs, all driven by human-specified design requirements. Experimental results for the task of object recognition showed that the AttoNets created via human-machine collaborative design has significantly fewer parameters and computational costs than state-of-the-art networks designed for efficiency while achieving noticeably higher accuracy (with the smallest AttoNet achieving ~1.8% higher accuracy while requiring ~10x fewer multiply-add operations and parameters than MobileNet-V1). Furthermore, the efficacy of the AttoNets is demonstrated for the task of instance segmentation and object detection, where an AttoNet-based Mask R-CNN network was constructed with significantly fewer parameters and computational costs (~5x fewer multiply-add operations and ~2x fewer parameters) than a ResNet-50 based Mask R-CNN network.

关键词： Neural networks Convolutional codes Man-machine systems Prototypes Collaboration Task analysis Network architecture

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Event-Based Eye Tracking. AIS 2024 Challenge Survey

Event-Based Eye Tracking. AIS 2024 Challenge Survey

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

作者： Zuowen Wang Chang Gao Zongwei Wu Marcos V. Conde Radu Timofte Shih-Chii Liu Qinyu Chen Zheng-jun Zha Wei Zhai Han Han Bohao Liao Yuliang Wu Zengyu Wan Zhong Wang Yang Cao Ganchao Tan Jinze Chen Yan Ru Pei Sasskia Brüers Sébastien Crouzet Douglas McLelland Oliver Coenen Baoheng Zhang Yizhao Gao Jingyuan Li Hayden Kwok-Hay So Philippe Bich Chiara Boretti Luciano Prono Mircea Lică David Dinucu-Jianu Cătălin Grîu Xiaopeng Lin Hongwei Ren Bojun Cheng Xinan Zhang Valentin Vial Anthony Yezzi James Tsai Institute of Neuroinformatics University of Zurich and ETH Zurich Delft University of Technology University of Würzburg Leiden University University of Science and Technology of China Brainchip Inc The University of Hong Kong Polytechnic of Turin The Hong Kong University of Science and Technology (Guangzhou) Georgia Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tracking with event cameras to achieve good task accuracy and efficiency trade-off. During the challenge period, 38 participants registered for the Kaggle competition, and 8 teams submitted a challenge factsheet. The novel and diverse methods from the submitted factsheets are reviewed and analyzed in this survey to advance future event-based eye tracking research.

关键词： Surveys computer vision Visualization Reviews Gaze tracking Cameras Transformers

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EfficientNet-SAM: A Novel EffecientNet with Spatial Attention Mechanism for COVID-19 Detection in Pulmonary CT Scans

EfficientNet-SAM: A Novel EffecientNet with Spatial Attentio...

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

作者： Ramy Farag Parth Upadhay Jacket Demby’s Yixiang Gao Katherin Garces Montoya Seyed Mohamad Ali Tousi Gbenga Omotara Guilherme DeSouza Department of Electrical Engineering and Computer Science Vision-Guided and Intelligent Robotics Lab - ViGIR Lab University of Missouri-Columbia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors, especially given high volume of patients and numerous images per patient. So, we address the need for automation of this task by developing a new deep learning-based pipeline. Our motivation was sparked by the CVPR Workshop on "Domain Adaptation, Explainability and Fairness in AI for Medical Image Analysis", more specifically, the "COVID-19 Diagnosis Competition (DEF-AI-MIA COV19D)" under the same Workshop. This challenge provides an opportunity to assess our proposed pipeline for COVID-19 detection from CT scan images. The same pipeline incorporates one of the architectures in the EfficientNet "family", but with an added Spatial Attention Mechanism: EfficientNet-SAM. Also, unlike the traditional/past pipelines, which relied on a preprocessing step, our pipeline takes the raw selected input images without any such step, except for an image-selection step to simply reduce the number of CT images required for training and/or testing. Moreover, our pipeline is computationally efficient, as, for example, it does not incorporate a decoder for segmenting the lungs. It also does not combine different models nor combine RNN with a backbone, as other pipelines in the past did. Nevertheless, our pipeline outperformed all approaches presented by other teams in last year’s instance of the same challenge using the validation subset. It also placed 5th in this year’s competition, ranking less than 1.3% below the 1st place and close to 3.5% above the 6th place based on the macro-F1 score.

关键词： COVID-19 Training Visualization Attention mechanisms Computed tomography conferences Pipelines

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Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report

Efficient Deep Models for Real-Time 4K Image Super-Resolutio...

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

作者： Marcos V. Conde Eduard Zamfir Radu Timofte Daniel Motilla Cen Liu Zexin Zhang Yunbo Peng Yue Lin Jiaming Guo Xueyi Zou Yuyi Chen Yi Liu Jia Hao Youliang Yan Yuanfan Zhang Gen Li Lei Sun Lingshun Kong Haoran Bai Jinshan Pan Jiangxin Dong Jinhui Tang Mustafa Ayazoglu Bahri Batuhan Bilecen Mingxi Li Yuhang Zhang Xianjun Fan Yankai Sheng Long Sun Zibin Liu Weiran Gou Shaoqing Li Ziyao Yi Yan Xiang Dehui Kong Ke Xu Ganzorig Gankhuyag Kihwan Yoon Jin Zhang Gaocheng Yu Feng Zhang Hongbin Wang Zhou Zhou Jiahao Chao Hongfan Gao Jiali Gong Zhengfeng Yang Zhenbing Zeng Chengpeng Chen Zichao Guo Anjin Park Yuqing Liu Qi Jia Hongyuan Yu Xuanwu Yin Kunlong Zuo Dongyang Zhang Ting Fu Zhengxue Cheng Shiai Zhu Dajiang Zhou Weichen Yu Lin Ge Jiahua Dong Yajun Zou Zhuoyuan Wu Binnan Han Xiaolin Zhang Heng Zhang Ben Shao Shaolong Zheng Daheng Yin Baijun Chen Mengyang Liu Marian-Sergiu Nistor Yi-Chung Chen Zhi-Kai Huang Yuan-Chun Chiang Wei-Ting Chen Hao-Hsiang Yang Hua-En Chang I-Hsiang Chen Chia-Hsuan Hsieh Sy-Yen Kuo Tu Vo Qingsen Yan Yun Zhu Jinqiu Su Yanning Zhang Cheng Zhang Jiaying Luo Youngsun Cho Nakyung Lee Computer Vision Lab University of Wurzburg Germany SIE FTG USA

This paper introduces a novel benchmark for efficient up-scaling as part of the NTIRE 2023 Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images from 720p and 1080p resolution to native 4K (×2 and ×3 factors) in real-time on commercial GPUs. For this, we use a new test set containing diverse 4K images ranging from digital art to gaming and photography. We assessed the methods devised for 4K SR by measuring their runtime, parameters, and FLOPs, while ensuring a minimum PSNR fidelity over Bicubic interpolation. Out of the 170 participants, 25 teams contributed to this report, making it the most comprehensive benchmark to date and showcasing the latest advancements in real-time SR.

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The 8th AI City Challenge

The 8th AI City Challenge

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

作者： Shuo Wang David C. Anastasiu Zheng Tang Ming-Ching Chang Yue Yao Liang Zheng Mohammed Shaiqur Rahman Meenakshi S. Arya Anuj Sharma Pranamesh Chakraborty Sanjita Prajapati Quan Kong Norimasa Kobori Munkhjargal Gochoo Munkh-Erdene Otgonbold Fady Alnajjar Ganzorig Batnasan Ping-Yang Chen Jun-Wei Hsieh Xunlei Wu Sameer Satish Pusegaonkar Yizhou Wang Sujit Biswas Rama Chellappa NVIDIA Corporation CA USA Santa Clara University CA USA University at Albany SUNY NY USA Australian National University Australia Iowa State University IA USA Indian Institute of Technology Kanpur India Woven by Toyota Japan United Arab Emirates University UAE Emirates Center for Mobility Research UAE National Yang-Ming Chiao-Tung University Taiwan Johns Hopkins University MD USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. Track 1 dealt with multi-target multi-camera (MTMC) people tracking, highlighting significant enhancements in camera count, character number, 3D annotation, and camera matrices, alongside new rules for 3D tracking and online tracking algorithm encouragement. Track 2 introduced dense video captioning for traffic safety, focusing on pedestrian accidents using multi-camera feeds to improve insights for insurance and prevention. Track 3 required teams to classify driver actions in a naturalistic driving analysis. Track 4 explored fish-eye camera analytics using the FishEye8K dataset. Track 5 focused on motorcycle helmet rule violation detection. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks, some surpassing existing state-of-the-art achievements.

关键词： computer vision Three-dimensional displays Pedestrians Prevention and mitigation Urban areas Cameras Safety

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NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

NTIRE 2022 Challenge on Super-Resolution and Quality Enhance...

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

作者： Ren Yang Radu Timofte Meisong Zheng Qunliang Xing Minglang Qiao Mai Xu Lai Jiang Huaida Liu Ying Chen Youcheng Ben Xiao Zhou Chen Fu Pei Cheng Gang Yu Junyi Li Renlong Wu Zhilu Zhang Wei Shang Zhengyao Lv Yunjin Chen Mingcai Zhou Dongwei Ren Kai Zhang Wangmeng Zuo Pavel Ostyakov Vyal Dmitry Shakarim Soltanayev Chervontsev Sergey Zhussip Magauiya Xueyi Zou Youliang Yan Pablo Navarrete Michelini Yunhua Lu Diankai Zhang Shaoli Liu Si Gao Biao Wu Chengjian Zheng Xiaofeng Zhang Kaidi Lu Ning Wang Thuong Nguyen Canh Thong Bach Qing Wang Xiaopeng Sun Haoyu Ma Shijie Zhao Junlin Li Liangbin Xie Shuwei Shi Yujiu Yang Xintao Wang Jinjin Gu Chao Dong Xiaodi Shi Chunmei Nian Dong Jiang Jucai Lin Zhihuai Xie Mao Ye Dengyan Luo Liuhan Peng Shengjie Chen Xin Liu Qian Wang Boyang Liang Hang Dong Yuhao Huang Kai Chen Xingbei Guo Yujing Sun Huilei Wu Pengxu Wei Yulin Huang Junying Chen Ik Hyun Lee Sunder Ali Khowaja Jiseok Yoon ETH Z&#x00FC rich rich University of W&#x00FC rzburg

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the super-resolution and quality enhancement of HEVC compressed video. They require x2 and x4 super-resolution, respectively. The three tracks totally attract more than 600 registrations. In the test phase, 8 teams, 8 teams and 12 teams submitted the final results to Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution and quality enhancement of compressed video. The proposed LDV 2.0 dataset is available at https://***/RenYang-home/LDV_dataset. The homepage of this challenge (including open-sourced codes) is at https://***/RenYang-home/NTIRE22_VEnh_SR.

关键词： computer vision Target tracking Codes conferences Superresolution pattern recognition

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HNN: Hierarchical Noise-Deinterlace Net Towards Image Denoising

HNN: Hierarchical Noise-Deinterlace Net Towards Image Denois...

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

作者： Amogh Joshi Nikhil Akalwadi Chinmayee Mandi Chaitra Desai Ramesh Ashok Tabib Ujwala Patil Uma Mudenagudi Center of Excellence in Visual Intelligence (CEVI) KLE Technological University Hubballi Karnataka India School of Computer Science and Engineering KLE Technological University Hubballi Karnataka India School of Electronics and Communication Engineering KLE Technological University Hubballi Karnataka India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we propose a hierarchical framework for image denoising and term it Hierarchical Noise-Deinterlace Net (HNN). Image denoising techniques aim to recover clean images from noisy observations by reducing unwanted noise and artifacts to enhance the clarity and introduce spatial coherence. Images captured during challenging scenarios suffer from granular noise, inducing fine-scale variations in the image, which occur due to the limitations of imaging technology or environmental conditions. This granular noise can significantly degrade the quality of the image, making it less useful for applications like object detection, image restoration/enhancement, face detection, and image super-resolution. From literature, we infer learning global-local features significantly contribute in reducing unwanted noise and artifacts within images. Typically, researchers rely on residual learning, Generative Adversarial Networks (GANs), and Attention Mechanisms to learn global-local features. However, these methods face challenges such as vanishing gradients, limited generalization of generators in GANs, lack of global context awareness and computation complexity in attention mechanisms leading to drop in performance. Towards this, we propose a hierarchical framework to process both global and local information across distinct levels of hierarchy. More specifically, we propose a hierarchical encoder-decoder network, with a distinct Global-Local Spatio-Contextual (GLSC) block for learning of fine-grained features and high-frequency details in an image. The proposed framework improves image denoising, as it allows the model to capture and utilize information from different scales, ensuring a comprehensive understanding of the image content. We demonstrate the efficacy of proposed HNN framework, on benchmark datasets in comparison with state-of-the-art methods with 5% (↑ in dB) increase in performance.

关键词： Attention mechanisms Noise Superresolution Spatial coherence Imaging Object detection Benchmark testing

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