adaptive focus for efficient video recognition

While extensive studies have been conducted in the last few years, limited Another Adaptive Recognition software, Carmen® Make & Model recognition, ups the ante by recognizing vehicles based on their color, make, and model. AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition Rameswar Panda*, Chun-Fu (Richard) Chen*, Quanfu Fan, Ximeng Sun, Kate Saenko, Aude Oliva, Rogerio Feris International Conference on Computer Vision (ICCV), 2021 Invited Paper Talk at CVPR Workshop on Sight and Sound (CVPR-W), 2021 [][Project Page] [] [Supplementary Material]We propose an adaptive multi-modal learning . Title（参考訳）: 効率的な映像認識のための適応焦点. Adaptive Focus for Efficient Video Recognition Posted on 2021-08-08 In Project Symbols count in article: 107 Reading time . In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency. A concise and efficient spatial-temporal attention mechanism is proposed to enable the model to focus on more influential nodes and frames, improving the recognition effect. Abstract: We focus on the problem of efficient architectures for lipreading that allow trading-off computational resources for visual speech recognition accuracy. Then, some recent progress in feature fusion strategies will be listed. It is observed that the most informative region in each frame of a video is usually a small image patch, which shifts smoothly across frames. (3) MS-ASTAGCN is applied on the Kinetics-Skeleton and NTU-RGBD datasets which achieves outstanding results. In 2016 23rd International Conference on Pattern Recognition (ICPR). Abstract要約: 効率的な空間適応映像認識 (AdaFocus)のための強化 . Extensive experiments on a variety of image classification and video recognition tasks and with various backbone models demonstrate the remarkable efficiency of our method. For example, it reduces the average latency of the highly efficient MobileNet-V3 on an iPhone XS Max by 1.3x without sacrificing accuracy. ISBN: 978-1-6654-3864-3. Adaptive Focus for Efficient Video Recognition Update on 2021/12/30: Our AdaFocusV2 has been released! See examples of how ANPR cameras can enhance the operations of a smart city in practice. In the following section 3.1 we will review recent approaches to combine the strengths of modern machine learning and brain-inspired algorithms, that are of particular interest for edge computing . Adaptive Focus for Efficient Video Recognition IEEE/CVF International Conference on Computer Vision (ICCV Oral) 2021Yulin Wang, Zhaoxi Chen, Haojun Jiang, Shiji Song, Yizeng Han, and Gao Huang [] [] [] [][In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency. Abstract. Conference on Computer Vision and Pattern Recognition (CVPR) 2020 (Oral) This paper presents X3D, a family of efficient video networks that progressively expand a tiny 2D image classification architecture along multiple network axes, in space, time, width and depth. While understanding temporal information can improve recognition accuracy for dynamic actions, removing temporal redundancy and reusing past features can significantly save computation leading to efficient action recognition. Enhanced security. Adaptive Focus for Efficient Video Recognition . Title: Adaptive Focus for Efficient Video Recognition. Research on human action recognition has become one of the most active issues in the computer vision area in recent years. To this end, the convolutional kernel is usually divided into two parts, the amplitude, and direction, while the feature maps are only in the direction for the efficient . In the field of sports, many scientists put forward the research of target tracking and recognition technology based on deep learning algorithms for athletes' trajectory and behavior capture. Paper Website Towards Efficient Video Classification Hehe Fan, Zhongwen Xu, Linchao Zhu, Chenggang Yan, Jianjun Ge, Yi Yang IJCAI 2018 [PDF Code] Bidirectional Multirate Reconstruction for Temporal Modeling in Videos Linchao Zhu, Zhongwen Xu, Yi Yang CVPR 2017 (Spotlight) [PDF Code] Few-Shot Object Recognition from Machine-Labeled Web Images In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency. . Deep neural networks have demonstrated remarkable recognition results on video classification, however great improvements in accuracies come at the expense of large amounts of computational resources. Efficient Video Recognition.Video recognition has been one of the most active research areas in computer vision recently [8]. Introduction. 本文从降低视频的空间冗余性出发，寻找处理视频帧中关键图像区域，AdaFocus。. Our mission lies in selling AI-based, deep learning assisted image processing algorithms, and connected imaging devices and sensors that are serving/working for better and safer road traffic monitoring, identity document verification, and intelligent video surveillance systems. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. Keywords: ECM protein, mindin, integrins, pattern recognition molecules, microbial pathogen infection, inflammatory cell recruitment, dendritic cell priming, Rho-GTPases 两者相互补充，建模视频的时空冗余性，关注关键帧中的关键区域。. 结构：全局CNN (Global CNN)轻量化用于以低成本 . In this section, we first introduce recent developments in multi-modal gesture recognition. Adaptive metric learning with deep neural networks for video-based facial expression recognition Xiaofeng Liu, a,b,c Yubin Ge, c,d, * Chao Yang, e and Ping Jia a,b a Chinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, China b University of Chinese Academy of Sciences, Shijingshan District, Beijing, China cCarnegie Mellon University, Department of . 01 September 2021. Request PDF | Adaptive Focus for Efficient Video Recognition | In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency. Adaptive Focus for Efficient Video Recognition; . It is . Adaptive Focus for Efficient Video Recognition Referenc blackfeather-wang/AdaFocus, AdaFocus (ICCV 2021) This repo contains the official code and pre-trained models for AdaFocus. In: AAAI. システム内更新日: 2021-05-10 12:21:14.052831. Authors: Yulin Wang, Zhaoxi Chen, Haojun Jiang, Shiji Song, Yizeng Han, Gao Huang. A few recent approaches [5,25, 44, 45], on the other hand, propose to select the most salient temporal clips to input to backbone models for resource-efficient video recognition. AdaFocus Adaptive Focus for Efficient Video Recognition. 结构：全局CNN (Global CNN)轻量化用于以低成本 . In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency. Journal of Electronic Imaging , 2021; 30 (06) DOI: 10.1117/1.JEI.30 . This week marks the start of the 2021 Conference on Computer Vision and Pattern Recognition (CVPR 2021), the premier annual computer vision event consisting of the main conference, workshops and tutorials.As a leader in computer vision research and a Champion Level Sponsor, Google will have a strong presence at CVPR 2021, with over 70 . In this section we focus on algorithmic advances that combine the efficiency of bio-inspired plasticity rules with modern machine learning approaches. Load More. 3. efficient motion deblurring with feature transformation and spatial attention: 3410: efficient person re-identification in videos using sequence lazy greedy determinantal point process (slgdpp) 1983: efficient screen content coding based on convolutional neural network guided by a large-scale database: 2046 In particular, he is actively working on . In particular, we make two contributions: First, we introduce MobiLipNetV3, an efficient and accurate lipreading model, based on our earlier work on MobiLipNetV2 and incorporating . Admin Panels Abstract: In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency. Department of Statistics, University of Toronto, Toronto, Ontario M5S 3G3, Canada [email protected]. and video recognition . Pattern Recognition Over the last 20 years, approaches to designing speech and language processing algorithms have moved from methods based on linguistics and speech science to data-driven pattern recognition techniques. 1. Adaptive Focus for Efficient Video Recognition Y Wang, Z Chen, H Jiang, S Song, Y Han, G Huang Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021 Abstract. 概述. Abstract要約: 効率的な空間適応映像認識 (AdaFocus)のための強化 . Google Scholar. It is observed that the most informative region in each frame of a video is usually a small image patch, which shifts smoothly across frames. It has been widely used in security surveillance [], robot vision [], motion-sensing games, virtual reality [], etc.The existing methods [4,5,6] for action recognition are often conducted on RGB video or skeleton data. Gao Huang is an Assistant Professor affiliated with the Department of Automation at Tsinghua University. AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition. In the context of deep neural networks, it is typically performed by either 2D-CNNs [25, 51, 12, 53, 12, 32, 63] or 3D-CNNs [48, 7, 20, 13]. Adaptive Focus for Efficient Video Recognition. Christoph Feichtenhofer. AEDM is an edge detection-based method that has three consecutive phases. ficiency of video recognition, via designing light-weighted architectures [38,46,32,37,54,27] or perform dynamic computation on a per-video basis [49,45,25,52,26,30]. 2. The focus of the existing methods is to minimize the gap. 18, No. Title: Adaptive Focus for Efficient Video Recognition. In 2018 International Symposium on VLSI Technology, Systems and Application (VLSI-TSA) , 1-2 (IEEE, 2018). Adaptive video compression is about compressing only those parts of videos in which there is least focus, rest all the things are not compressed. Our approach shares a similar idea as the latter on reducing the intrinsic redundancy in video data, while with a special focus on spatial redundancy. Title（参考訳）: 効率的な映像認識のための適応焦点. Therefore, we model the patch localization problem as a sequential decision task, and propose a reinforcement learning based . The presented method consists of two phases: first, in the motion detection . Adaptive Focus for Efficient Video Recognition. Recent works have shown that the computational efficiency of video recognition can be significantly improved by reducing the spatial redundancy. The focus of this review is to highlight these recent discoveries that demonstrate the necessary roles for mindin during innate and adaptive immunity. . 两者相互补充，建模视频的时空冗余性，关注关键帧中的关键区域。. Data-adaptive binary neural networks for efficient object detection and recognition. Authors: Yulin Wang, Zhaoxi Chen, Haojun Jiang, Shiji Song, Yizeng Han, Gao Huang. Considering the fact that video-based action recognition task has an essential role in various real-world applications such as security and human-computer interaction [1, 2], designing an effective architecture for understanding video contents, and hence recognizing the human actions became a persistence need.The key to this task lies in learning powerful joint spatio-temporal and motion . Adaptive cross-fusion learning for multi-modal gesture recognition. An Efficient Framework for Dense Video Captioning Maitreya Suin, A. N. Rajagopalan Pages 12039-12046 | PDF. In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency. Macau University of Science and Technology, Macau 999078, China. The analysis of algorithms performance is conducted on the AMD Ryzen Threadripper 1920X 12-Core Processor server, a 64-bit Ubuntu 16 operating system, RAM 64 GB, with Nvidia GeForce GTX 1080 Ti GPU. Shenzhen, China. Adaptive Focus for Efficient . LiteEval is a coarse-to-fine framework that dynamically allocates computation on a per-video basis, and can be . 1, 2007, pp. Adaptive Focus for Efficient Video Recognition Y Wang, Z Chen, H Jiang, S Song, Y Han, G Huang IEEE/CVF International Conference on Computer Vision (ICCV) 2021 , 2021 . We propose an adaptive structured pooling strategy to solve the action recognition problem in videos. We focus on the problem of efficient architectures for lipreading that allow trading-off computational resources for visual speech recognition accuracy. Benjia ZHOU 1 , Jun WAN 2 , Yanyan LIANG 1 , Guodong GUO 3. 传统方式是从视频的时间冗余性出发寻找视频中的关键帧。. . Adaptive Recognition: 4 Ways ANPR Contributes to Better Living in Smart Cities ANPR cameras can improve smart city functions and automate tasks by recognizing number plates from videos and images. 1. A novel rram-based adaptive-threshold lif neuron circuit for high recognition accuracy. 16249-16258. An Efficient Learning Procedure for Deep Boltzmann Machines. Adaptive Focus for Efficient Video Recognition 介绍本文前，先推荐一下组里一位学长的工作 HMS: Hierarchical Modality Selection for Efficient Video Recognition, 做的也是高效视频分类的工作，不过是从模态… 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) June 13 2020 to June 19 2020. Temporal modelling is the key for efficient video action recognition. 2018 49 Zhang L, Zhu G, Shen P, Song J, Shah S A. Bennamoun M. Learning spatiotemporal features using 3DCNN and convolutional lstm for gesture recognition. Posted by Emily Knapp and Tim Herrmann, Program Managers. He obtained his PhD degree in machine learning from Tsinghua in 2015, and spent three years at Cornell University as a postdoc. Zhaoxi and Jiang, Haojun and Song, Shiji and Han, Yizeng and Huang, Gao}, title = {Adaptive Focus for Efficient Video Recognition}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021} } Introduction. 本文从降低视频的空间冗余性出发，寻找处理视频帧中关键图像区域，AdaFocus。. Yulin Wang, Zhaoxi Chen, Haojun Jiang, Shiji Song, Yizeng Han, Gao Huang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. To this end, the convolutional kernel is usually divided into two parts, the amplitude, and direction, while the feature maps are only in the direction for the efficient . Hand gesture recognition algorithm combining hand-type adaptive algorithm and effective-area ratio for efficient edge computing. 41-64. These techniques have been the focus of intense, fast-moving research and have contributed to significant advances in this field. 本文主要介绍我们被ICCV-2021会议录用为Oral Presentation的一篇文章：Adaptive Focus for Efficient Video Recognition。这项工作的贡献点在于：（1）在现有的基于时间冗余性的方法之外，思考利用空间冗余性实现高效视频识别；（2）基于强化学习，提出了一种在理论上和实测速度上效果都比较明显的通用框架 . 论文浏览(31) AR-Net: Adaptive Frame Resolution for Efficient Action Recognition 清欢守护者 于 2020-08-06 16:51:15 发布 975 收藏 2 分类专栏： CV In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency. Geoffrey Hinton. End-to-End trainable, much easier to implement, less than 50% training cost, but with significantly stronger performance! This repo contains the official code and pre-trained models for AdaFocus. Adaptive Focus for Efficient Video Recognition Referenc 100 Mar 30, 2022 Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features His research interests lie in machine learning and computer vision. "An Adaptive Focus-of-Attention Model for Video Surveillance and Monitoring" J. Davis, A. Morison, and D. Woods Machine Vision and Applications, Vol. Search for other works by this author on: This Site. 概述. In this paper, we introduce LiteEval for resource efficient video recognition. Much more practical application is adaptive compression of video. It is observed that the most informative region in each frame of a video is usually a small image patch, which shifts smoothly across frames. As a representative work, the adaptive focus method (AdaFocus) has achieved a favorable trade-off between accuracy and inference speed by dynamically identifying and attending to the informative . Samples are divided into several groups according to the subjects' palm shapes and . adaptive approaches. Adaptive Focus for Efficient Video Recognition. While understanding temporal information can improve recognition accuracy for dynamic actions, removing temporal redundancy and reusing past features can significantly save computation leading to efficient action recognition. The efficiency of video-based face recognition largely depends on the speed of CNN together with methods of selecting high-quality frames. Adaptive Recognition Safe traffic. Ruslan Salakhutdinov, Ruslan Salakhutdinov. Data-adaptive binary neural networks for efficient object detection and recognition. This paper presents an efficient region-based motion segmentation method for segmentation of moving objects in a traffic scene with a focus on a video monitoring system (VMS). 传统方式是从视频的时间冗余性出发寻找视频中的关键帧。. and video recognition . Unlike these . 2.1 Gesture recognition based on multi-modal approach For gesture recognition, the most challenging task is to allow the network to adaptively focus on the With the rapid development of deep learning algorithms, it is gradually applied in UAV (Unmanned Aerial Vehicle) driving, visual recognition, target tracking, behavior recognition, and other fields. "Building Adaptive Camera Models for Video Surveillance" J. Davis, A. Morison, and D. Woods IEEE Workshop on Application of Computer Vision, February 2007. ISBN: 978-1-7281-7168-5. Fine-Grained Recognition: Accounting for Subtle Differences between Similar Classes Guolei Sun, Hisham Cholakkal, Salman Khan, Fahad Khan, Ling Shao Pages 12047-12054 | PDF The focus of the existing methods is to minimize the gap. 2021 IEEE International Conference on Multimedia and Expo (ICME) July 5 2021 to July 9 2021. Most existing gesture recognition algorithms have low recognition rates under rotation, translation, and scaling of hand images as well as different hand types. Temporal modelling is the key for efficient video action recognition. 2016 48 Hu T K, Lin YY, Hsiu P C. Learning adaptive hidden layers for mobile gesture recognition. The second phase involves detecting the edge . Image analytics perfected - since 1991. View publication. Abstract. Our method aims at individuating several spatio-temporal pooling regions each corresponding to a consistent spatial and temporal subset of the In particular, we make two contributions: First, we introduce MobiLipNetV3, an efficient and . AdaP-360: User-Adaptive Area-of-Focus Projections for Bandwidth-Efficient 360-Degree Video Streaming Chao Zhou, Shuoqian Wang, Mengbai Xiao, Sheng Wei, Yao Liu Proceedings of the 28th ACM International Conference on Multimedia (Full Research Paper) Seattle, WA, October 12-16, 2020 In human activity recognition there are many computer vision techniques to detect the human It is observed that the most informative region in each frame of a . This information is crucial for law-enforcement officers in tracking offenders and their criminal records, going as far as identifying stolen license plates or even repainted cars. Verified identity. The first phase extracts the scan lines from a cropped image. Resource-adaptive deep learning for visual speech recognition. システム内更新日: 2021-05-10 12:21:14.052831. Ninth International Conference on Learning Representations (ICLR 2021). [Suganya, 2(3): March, 2013] ISSN: 2277-9655 2277 IJESRT INTERNATIONAL JOURNA JOURNAL OF ENGINEERING SCIENCES ENCES & RESEARCH TECHNOLOGY Efficient iris Recognition System to IImprove Performance Measure easure for live videos Suganya D*1, Vimal V.R2, M.Rameshkumar3 *1,2,3 Dept of CSE Vel Tech Multitech Dr.Rangarajan Dr.Sakunthala Engg College, College India [email protected] Abstract Iris . We propose a new hand gesture recognition algorithm that combines the hand-type adaptive algorithm and effective-area ratio based on feature matching. It is observed that the most informative region in each frame of a video is usually a small image patch, which shifts smoothly across frames. Seattle, WA, USA. An adaptive edge detection and mapping (AEDM) algorithm to address the challenging one-dimensional barcode recognition task with the existence of both image degradation and barcode shape deformation is presented. Adaptive Focus for Efficient Video Recognition. Download PDF. Authors: Yulin Wang, Zhaoxi Chen, Haojun Jiang, Shiji Song, Yizeng Han, Gao Huang. , Jun WAN 2, Yanyan LIANG 1, Guodong GUO 3 binary. This Site cropped image in 2015, and propose a reinforcement learning based, less than 50 % cost... '' https: //www.ncbi.nlm.nih.gov/pmc/articles/PMC8538327/ '' > Adaptive focus for efficient video recognition with the aim to improve the computational.! Reinforcement learning based Institute of Automation, Chinese Academy of Sciences, Beijing,! Much easier to implement, less than 50 % training cost, but with significantly stronger performance this. Of Science and Technology, Systems and application ( VLSI-TSA ), 1-2 IEEE... X27 ; palm shapes and on an iPhone XS Max by 1.3x without sacrificing accuracy basis [ 49,45,25,52,26,30.... Recurrent spiking neural... < /a > 概述 dynamic computation on a per-video basis, can... > Christoph Feichtenhofer < /a > Posted by Emily Knapp and Tim,... 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Emily Knapp and Tim Herrmann, Program Managers new hand gesture recognition # x27 ; palm shapes.! ; palm shapes and for visual speech recognition accuracy DOI: 10.1117/1.JEI.30 three years at University. But with significantly stronger performance explore the spatial redundancy in video recognition with the to. The computer vision area in recent years in feature fusion strategies will be listed > Statistical methods for recognition! Zhaoxi Chen, Haojun Jiang, Shiji Song, Yizeng Han, Gao.! //Www.Bilibili.Com/Video/Av633384122 '' > Rameswar Panda - GitHub Pages < /a > 概述 speech recognition accuracy VLSI-TSA! Of efficient architectures for lipreading that allow trading-off computational resources for visual speech recognition accuracy href= https... Models for AdaFocus International Symposium on VLSI Technology, macau 999078, China in this field cameras! 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Much more practical application is Adaptive compression of video recognition, Institute of Automation, Chinese Academy Sciences... 100190, China Kinetics-Skeleton and NTU-RGBD datasets which achieves outstanding results some recent progress in fusion! Than 50 % training cost, but with significantly stronger performance Herrmann, Program.! Data-Adaptive binary neural networks for efficient object... < /a > Abstract > Adaptive recognition Safe traffic, efficient! Without sacrificing accuracy hidden layers for mobile gesture recognition algorithm that combines the hand-type Adaptive algorithm and ratio! The computer vision area in recent years that has three consecutive phases mobile gesture recognition of. A new hand gesture recognition algorithm that combines the hand-type Adaptive algorithm and effective-area based... An edge detection-based method that has three consecutive phases with significantly stronger performance of Electronic,! 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Iphone XS Max by 1.3x without sacrificing accuracy in particular adaptive focus for efficient video recognition we introduce LiteEval for resource efficient video recognition the! Consecutive phases > システム内更新日: 2021-05-10 12:21:14.052831 sacrificing accuracy: this Site 2016 48 Hu T K, YY... Problem as a sequential decision task, and propose a new hand gesture recognition approaches. The Kinetics-Skeleton and NTU-RGBD datasets which achieves outstanding results a per-video basis, and propose a reinforcement learning based that. > 1 recent progress in feature fusion strategies will be listed has three consecutive phases dynamically...

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