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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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Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware

Retina : Low-Power Eye Tracking with Event Camera and Spikin...

引用

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

作者： Pietro Bonazzi Sizhen Bian Giovanni Lippolis Yawei Li Sadique Sheik Michele Magno ETH Zürich Inivation AG Synsense AG

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a neuromorphic dataset and methodology for eye tracking, harnessing event data captured streamed continuously by a Dynamic vision Sensor (DVS). The framework integrates a directly trained Spiking Neuron Network (SNN) regression model and leverages a state-of-the-art low power edge neuromorphic processor - Speck. First, it introduces a representative event-based eye-tracking dataset, "Ini-30," which was collected with two glass-mounted DVS cameras from thirty volunteers. Then, a SNN model, based on Integrate And Fire (IAF) neurons, named "Retina", is described , featuring only 64k parameters (6.63x fewer than 3ET) and achieving pupil tracking error of only 3.24 pixels in a 64x64 DVS input. The continuous regression output is obtained by means of temporal convolution using a non-spiking 1D filter slided across the output spiking layer over time. Retina is evaluated on the neuromorphic processor, showing an end-to-end power between 2.89-4.8 mW and a latency of 5.57-8.01 ms dependent on the time to slice the event-based video recording. The model is more precise than the latest event-based eye-tracking method, "3ET", on Ini-30, and shows comparable performance with significant model compression (35 times fewer MAC operations) in the synthetic dataset used in "3ET". We hope this work will open avenues for further investigation of close-loop neuromorphic solutions and true event-based training pursuing edge performance.

关键词： Neuromorphics Computational modeling Neurons Gaze tracking vision sensors Retina Cameras

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HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution

HMANet: Hybrid Multi-Axis Aggregation Network for Image Supe...

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

作者： Shu-Chuan Chu Zhi-Chao Dou Jeng-Shyang Pan Shaowei Weng Junbao Li College of Computer Science and Engineering Shandong University of Science and Technology School of Artificial Intelligence Nanjing University of Information Science and Technology School of Information Engineering Guangdong University of Technology School of Electronic and Information Engineering Harbin Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Transformer-based methods have demonstrated excellent performance on super-resolution visual tasks, surpassing conventional convolutional neural networks. However, existing work typically restricts self-attention computation to non-overlapping windows to save computational costs. This means that Transformer-based networks can only use input information from a limited spatial range. Therefore, a novel Hybrid Multi-Axis Aggregation network (HMA) is proposed in this paper to exploit feature potential information better. HMA is constructed by stacking Residual Hybrid Transformer Blocks(RHTB) and Grid Attention Blocks(GAB). On the one side, RHTB combines channel attention and self-attention to enhance nonlocal feature fusion and produce more attractive visual results. Conversely, GAB is used in cross-domain information interaction to jointly model similar features and obtain a larger perceptual field. For the super-resolution task in the training phase, a novel pre-training method is designed to enhance the model representation capabilities further and validate the proposed model’s effectiveness through many experiments. The experimental results show that HMA outperforms the state-of-the-art methods on the benchmark dataset. We provide code and models at https://***/korouuuuu/HMA.

关键词： Training Visualization Convolution Superresolution Stacking Benchmark testing Transformers

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Deploying Machine Learning Anomaly Detection Models to Flight Ready AI Boards

Deploying Machine Learning Anomaly Detection Models to Fligh...

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

作者： James Murphy Maria Buckley Léonie Buckley Adam Taylor Jake O’Brien Brian Mac Namee Réaltra Space Systems Engineering Ubotica Technologies Adiuvo Engineering & Training School of Computer Science University College Dublin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study explores the development and implementation of machine learning (ML) models on Edge-AI boards, aiming to identify the most effective solution for anomaly detection systems on space missions. We investigate ML anomaly detection techniques including Autoencoders, Long Short-Term Memory (LSTM) cells, Isolation Forests, and Transformers. These models were trained on a univariate dataset derived from real space missions and deployed on diverse hardware platforms engineered for space environments to comprehensively assess performance. Specifically, we explore space flight ready boards (Ubotica CogniSAT-XE1 and XE2, which incorporate Intel’s Myriad 2 and X chips, respectively); commercial, non-space flight ready, edge-AI boards (NVIDIA’s Jetson Nano and Google Coral); and Field Programmable Gate Array (FPGA) implementations (from Microchip, AMD, and NanoXplore). We compare the performance of anomaly detection models run on space flight ready and commercial boards (using CPU performance as a benchmark) to provide a thorough comparison of available platforms for onboard anomaly detection. This paper provides a detailed examination of both the optimal ML models and hardware platforms for deploying univariate anomaly detection systems in space flight contexts and draws conclusions about which ones are most suitable.

关键词： Performance evaluation Space missions Image edge detection Computational modeling Machine learning Transformers Hardware

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End-to-End Lane Marker Detection via Row-wise Classification

End-to-End Lane Marker Detection via Row-wise Classification

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

作者： Seungwoo Yoo Hee Seok Lee Heesoo Myeong Sungrack Yun Hyoungwoo Park Janghoon Cho Duck Hoon Kim Qualcomm Korea YH

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In autonomous driving, detecting reliable and accurate lane marker positions is a crucial yet challenging task. The conventional approaches for the lane marker detection problem perform a pixel-level dense prediction task followed by sophisticated post-processing that is inevitable since lane markers are typically represented by a collection of line segments without thickness. In this paper, we propose a method performing direct lane marker vertex prediction in an end-to-end manner, i.e., without any post-processing step that is required in the pixel-level dense prediction task. Specifically, we translate the lane marker detection problem into a row-wise classification task, which takes advantage of the innate shape of lane markers but, surprisingly, has not been explored well. In order to compactly extract sufficient information about lane markers which spread from the left to the right in an image, we devise a novel layer, inspired by [8], which is utilized to successively compress horizontal components so enables an end-to-end lane marker detection system where the final lane marker positions are simply obtained via argmax operations in testing time. Experimental results demonstrate the effectiveness of the proposed method, which is on par or outperforms the state-of-the-art methods on two popular lane marker detection benchmarks, i.e., TuSimple and CULane.

关键词： Task analysis Image segmentation computer architecture Semantics Visualization Spatial resolution Computational complexity

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Do More With What You Have: Transferring Depth-Scale from Labeled to Unlabeled Domains

Do More With What You Have: Transferring Depth-Scale from La...

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

作者： Alexandra Dana Nadav Carmel Amit Shomer Ofer Manela Tomer Peleg Samsung Israel R&D Center Tel Aviv Israel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Transferring the absolute depth prediction capabilities of an estimator to a new domain is a task with significant real-world applications. This task is specifically challenging when images from the new domain are collected without ground-truth depth measurements, and possibly with sensors of different intrinsics. To overcome such limitations, a recent zero-shot solution was trained on an extensive training dataset and encoded the various camera intrinsics. Other solutions generated synthetic data with depth labels that matched the intrinsics of the new target data to enable depth-scale transfer between the *** this work we present an alternative solution that can utilize any existing synthetic or real dataset, that has a small number of images annotated with ground truth depth labels. Specifically, we show that self-supervised depth estimators result in up-to-scale predictions that are linearly correlated to their absolute depth values across the domain, a property that we model in this work using a single scalar. In addition, aligning the field-of-view of two datasets prior to training, results in a common linear relationship for both domains. We use this observed property to transfer the depth-scale from source datasets that have absolute depth labels to new target datasets that lack these measurements, enabling absolute depth predictions in the target *** suggested method was successfully demonstrated on the KITTI, DDAD and nuScenes datasets, while using other existing real or synthetic source datasets, that have a different field-of-view, other image style or structural content, achieving comparable or better accuracy than other existing methods that do not use target ground-truth depths.

关键词： Training Image sensors computer vision Accuracy conferences Predictive models Cameras

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NTIRE 2023 Challenge on Image Denoising: Methods and Results

NTIRE 2023 Challenge on Image Denoising: Methods and Results

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

作者： Yawei Li Yulun Zhang Radu Timofte Luc Van Gool Zhijun Tu Kunpeng Du Hailing Wang Hanting Chen Wei Li Xiaofei Wang Jie Hu Yunhe Wang Xiangyu Kong Jinlong Wu Dafeng Zhang Jianxing Zhang Shuai Liu Furui Bai Chaoyu Feng Hao Wang Yuqian Zhang Guangqi Shao Xiaotao Wang Lei Lei Rongjian Xu Zhilu Zhang Yunjin Chen Dongwei Ren Wangmeng Zuo Qi Wu Mingyan Han Shen Cheng Haipeng Li Ting Jiang Chengzhi Jiang Xinpeng Li Jinting Luo Wenjie Lin Lei Yu Haoqiang Fan Shuaicheng Liu Aditya Arora Syed Waqas Zamir Javier Vazquez-Corral Konstantinos G. Derpanis Michael S. Brown Hao Li Zhihao Zhao Jinshan Pan Jiangxin Dong Jinhui Tang Bo Yang Jingxiang Chen Chenghua Li Xi Zhang Zhao Zhang Jiahuan Ren Zhicheng Ji Kang Miao Suiyi Zhao Huan Zheng YanYan Wei Kangliang Liu Xiangcheng Du Sijie Liu Yingbin Zheng Xingjiao Wu Cheng Jin Rajeev Irny Sriharsha Koundinya Vighnesh Kamath Gaurav Khandelwal Sunder Ali Khowaja Jiseok Yoon Ik Hyun Lee Shijie Chen Chengqiang Zhao Huabin Yang Zhongjian Zhang Junjia Huang Yanru Zhang Computer Vision Lab ETH Zurich

This paper reviews the NTIRE 2023 challenge on image denoising (σ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image denoising. Independent additive white Gaussian noise (AWGN) is assumed and the noise level is 50. The challenge had 225 registered participants, and 16 teams made valid submissions. They gauge the state-of-the-art for image denoising.

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Uncovering Hidden Emotions with Adaptive Multi-Attention Graph Networks

Uncovering Hidden Emotions with Adaptive Multi-Attention Gra...

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

作者： Ankith Jain Rakesh Kumar Bir Bhanu Department of Electrical and Computer Engineering University of California Riverside

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Micro-expressions (MEs) are subtle expressions lasting a fraction of a second, offering valuable cues for understanding human emotions and intentions. However, effectively classifying these subtle expressions from video data poses several challenges due to their short duration and low intensity. This paper addresses these issues and presents a novel 2-stream Adaptive Multi-Attention ((Self-Attention and Gaussian Attention) Graph Network (2S-AMAGN) based approach for ME classification in videos. The Self-Attention mechanism captures the global and local dependencies between nodes in a graph. The Gaussian attention mechanism computes weights based on the Gaussian distribution, considering the mean and variance of features across each edge, offering a nuanced understanding of spatial and temporal relationships within MEs. It meticulously analyzes node pair features and edge features, capturing the significance of facial regions. An adaptive learnable weight is introduced to learn the contributions of each attention mechanism, facilitating adaptive attention fusion. The network utilizes a three-frame graph structure to extract spatio-temporal information. The approach incorporates a dynamic frame selection mechanism, which utilizes a sliding window optical flow method to filter out low-intensity emotion frames, thereby refining the extraction of spatio-temporal information from the video data. The results are presented and compared with state-of-the-art methods for SMIC and SAMM databases. Additionally, cross-dataset experiments are conducted, and the results are reported.

关键词： Optical filters Attention mechanisms Adaptive systems Databases Refining Gaussian distribution Information filters

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NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Ancuti, Codruta O. Ancuti, Cosmin Vasluianu, Florin-Alexandru Timofte, Radu Zhou, Han Dong, Wei Liu, Yangyi Chen, Jun Liu, Huan Li, Liangyan Wu, Zijun Dong, Yubo Li, Yuyan Qiu, Tian He, Yu Lu, Yonghong Wu, Yinwei Jiang, Zhenxiang Liu, Songhua Yang, Xingyi Jing, Yongcheng Benjdira, Bilel Ali, Anas M. Koubaa, Anis Yang, Hao-Hsiang Chen, I-Hsiang Chen, Wei-Ting Huang, Zhi-Kai Chen, Yi-Chung Hsieh, Chia-Hsuan Chang, Hua-En Chiang, Yuan-Chun Kuo, Sy-Yen Guo, Yu Gao, Yuan Liu, Ryan Wen Lu, Yuxu Qu, Jingxiang He, Shengfeng Ren, Wenqi Hoang, Trung Zhang, Haichuan Yazdani, Amirsaeed Monga, Vishal Yang, Lehan Wu, Alex Jiahao Mai, Tiancheng Cong, Xiaofeng Yin, Xuemeng Yin, Xuefei Emad, Hazim Abdallah, Ahmed Yasser, Yahya Elshahat, Dalia Elbaz, Esraa Li, Zhan Kuang, Wenqing Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Zhang, Zhao Wei, Yanyan Wang, Junhu Zhao, Suiyi Zheng, Huan Guo, Jin Sun, Yangfan Liu, Tianli Hao, Dejun Jiang, Kui Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph ETcTI Universitatea Politehnica Timisoara Romania ICTEAM UCL Belgium Computer Vision Lab University of Wuerzburg Germany Computer Vision Lab ETH Zurich Switzerland Department of Electrical and Computer Engineering McMaster University Canada Department of Electrical and Computer Engineering University of Alberta Canada McMaster University Canada Xidian University China Research Institute Singapore National University of Singapore Singapore University of Sydney Australia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Wuhan University of Technology China Singapore Management University Singapore Singapore Sun Yat-sen University China Electrical Engineering Department Pennsylvania State University United States The University of Sydney Australia Southeast University China University of California Los Angeles United States Beijing Jiaotong University China Mansoura Univeristy Egypt College of Information Science and Technology Jinan University China Department of Information Technology Uppsala University Sweden Hefei University of Technology China Zhejiang Dahua Technology China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50 high resolution pairs of real-life outdoor images featuring nonhomogeneous hazy images and corresponding haze-free images of the same scene. The nonhomogeneous haze was simulated using a professional setup that replicated real-world conditions of hazy scenarios. The competition had 246 participants and 17 teams submitted solutions for the final testing phase. The proposed solutions demonstrated the cutting-edge in image dehazing technology. © 2023 ieee.

关键词： Demulsification

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Generative Feature Replay For Class-Incremental Learning

Generative Feature Replay For Class-Incremental Learning

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

作者： Xialei Liu Chenshen Wu Mikel Menta Luis Herranz Bogdan Raducanu Andrew D. Bagdanov Shangling Jui Joost van de Weijer Computer Vision Center Universitat Autonoma de Barcelona Barcelona Spain Media Integration and Communication Center University of Florence Florence Italy Huawei Kirin Solution China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We consider a class-incremental setting which means that the task-ID is unknown at inference time. The imbalance between old and new classes typically results in a bias of the network towards the newest ones. This imbalance problem can either be addressed by storing exemplars from previous tasks, or by using image replay methods. However, the latter can only be applied to toy datasets since image generation for complex datasets is a hard *** propose a solution to the imbalance problem based on generative feature replay which does not require any exemplars. To do this, we split the network into two parts: a feature extractor and a classifier. To prevent forgetting, we combine generative feature replay in the classifier with feature distillation in the feature extractor. Through feature generation, our method reduces the complexity of generative replay and prevents the imbalance problem. Our approach is computationally efficient and scalable to large datasets. Experiments confirm that our approach achieves state-of-the-art results on CIFAR-100 and ImageNet, while requiring only a fraction of the storage needed for exemplar-based continual learning. Code available at https://***/xialeiliu/GFR-IL.

关键词： Task analysis Feature extraction Gallium nitride Image generation Correlation Training Generators

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Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation

Dedicated Inference Engine and Binary-Weight Neural Networks...

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

作者： Tse-Wei Chen Wei Tao Dongyue Zhao Kazuhiro Mima Tadayuki Ito Kinya Osa Masami Kato Device Technology Development Headquarters Canon Inc. Tokyo Japan Canon Innovative Solution (Beijing) Co. Ltd. Beijing China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Binary-weight Neural Networks (BNNs), in which weights are binarized and activations are quantized, are employed to reduce computational costs of various kinds of applications. In this paper, a design methodology of hardware architecture for inference engines is proposed to handle modern BNNs with two operation modes. Multiply-Accumulate (MAC) operations can be simplified by replacing multiply operations with bitwise operations. The proposed method can effectively reduce the gate count of inference engines by removing a part of computational costs from the hardware system. The architecture of MAC operations can calculate the inference results of BNNs efficiently with only 52% of hardware costs compared with the related works. To show that the inference engine can handle practical applications, two lightweight networks which combine the backbones of SegNeXt and the decoder of SparseInst for instance segmentation are also proposed. The output results of the lightweight networks are computed using only bitwise operations and add operations. The proposed inference engine has lower hardware costs than related works. The experimental results show that the proposed inference engine can handle the proposed instance-segmentation networks and achieves higher accuracy than YOLACT on the "Person" category although the model size is 77.7× smaller compared with YOLACT.

关键词： Instance segmentation Costs Accuracy Design methodology Neural networks computer architecture Logic gates

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