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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是1071-1080 订阅

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Low-latency hand gesture recognition with a low resolution thermal imager

Low-latency hand gesture recognition with a low resolution t...

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

作者： Vandersteegen, Maarten Reusen, Wouter Van Beeck, Kristof Goedeme, Toon Katholieke Univ Leuven EAVISE Campus De Nayer Leuven Belgium Melexis Technol Nv Leuven Belgium

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Using hand gestures to answer a call or to control the radio while driving a car, is nowadays an established feature in more expensive cars. High resolution time-of-flight cameras and powerful embedded processors usually form the heart of these gesture recognition systems. This however comes with a price tag. We therefore investigate the possibility to design an algorithm that predicts hand gestures using a cheap low-resolution thermal camera with only 32x24 pixels, which is light-weight enough to run on a low-cost processor. We recorded a new dataset of over 1300 video clips for training and evaluation and propose a light-weight low-latency prediction algorithm. Our best model achieves 95.9% classification accuracy and 83% mAP detection accuracy while its processing pipeline has a latency of only one frame.

关键词： Thermal sensors Cameras Two dimensional displays Three-dimensional displays Gesture recognition Automobiles

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Improving In-field Cassava Whitefly Pest Surveillance with Machine Learning

Improving In-field Cassava Whitefly Pest Surveillance with M...

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

作者： Tusubira, Jeremy Francis Nsumba, Solomon Ninsiima, Flavia Akera, Benjamin Acellam, Guy Nakatumba, Joyce Mwebaze, Ernest Quinn, John Oyana, Tonny Makerere Univ Artificial Intelligence Lab Kampala Uganda Google Res Mountain View CA USA Makerere Univ Geospatial Data & Computat Intelligence Lab Kampala Uganda

ISBN: (纸本)9781728193601

Whiteflies are the major vector responsible for the transmission of cassava related diseases in tropical environments, and knowing the numbers of whiteflies is key in detecting and identifying their spread and prevention. However, the current approach for counting whiteflies is a simple visual inspection, where a cassava leaf is turned upside down to reveal the underside where the whiteflies reside to enable a manual count. Repeated across many cassava farms, this task is quite tedious and time-consuming. In this paper, we propose a method to automatically count whiteflies using computer vision techniques. To implement this approach, we collected images of infested cassava leaves and trained a computer vision detector using Haar Cascade and Deep Learning techniques. The two techniques were used to identify the pest in images and return a count. Our results show that this novel method produces a white-fly count with high precision. This method could be applied to similar object detection scenarios similar to the whitefly problem with minor adjustments.

关键词： computer vision

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Decoupling Video and Human Motion: Towards Practical Event Detection in Athlete Recordings

Decoupling Video and Human Motion: Towards Practical Event D...

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

作者： Einfalt, Moritz Lienhart, Rainer Univ Augsburg Augsburg Germany

ISBN: (纸本)9781728193601

In this paper we address the problem of motion event detection in athlete recordings from individual sports. In contrast to recent end-to-end approaches, we propose to use 2D human pose sequences as an intermediate representation that decouples human motion from the raw video information. Combined with domain-adapted athlete tracking, we describe two approaches to event detection on pose sequences and evaluate them in complementary domains: swimming and athletics. For swimming, we show how robust decision rules on pose statistics can detect different motion events during swim starts, with a F-1 score of over 91% despite limited data. For athletics, we use a convolutional sequence model to infer stride-related events in long and triple jump recordings, leading to highly accurate detections with 96% in F-1 score at only +/- 5ms temporal deviation. Our approach is not limited to these domains and shows the flexibility of pose-based motion event detection.

关键词： computer vision

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Image2Audio: Facilitating Semi-supervised Audio Emotion recognition with Facial Expression Image

Image2Audio: Facilitating Semi-supervised Audio Emotion Reco...

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

作者： He, Gewen Liu, Xiaofeng Fan, Fangfang You, Jane Florida State Univ Tallahassee FL 32306 USA Harvard Univ Cambridge MA 02138 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA HK PolyU Hong Kong Peoples R China

ISBN: (纸本)9781728193601

There is a large amount of public available labeled image-based facial expression recognition datasets. How could these images help for the audio emotion recognition with limited labeled data according to their inherent correlations can be a meaningful and challenging task. In this paper, we propose a semi-supervised adversarial network that allows the knowledge transfer from the labeled videos to the heterogeneous labeled audio domain hence enhancing the audio emotion recognition performance. Specifically, face image samples are translated to the spectrograms class-wisely. To harness the translated samples in a sparsely distributed area and construct a tighter decision boundary, we propose to precisely estimate the density on feature space and incorporate the reliable low-density sample with an annealing scheme. Moreover, the unlabeled audios are collected with the high-density path in a graph representation. As a possible "recognition via generation" framework, we empirically demonstrated its effectiveness on several audio emotional recognition benchmarks.

关键词： Emotion recognition

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RIT-18: A Novel Dataset for Compositional Group Activity Understanding

RIT-18: A Novel Dataset for Compositional Group Activity Und...

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

作者： Chen, Junwen Hao, Haiting Hong, Hanbin Kong, Yu Rochester Inst Technol Golisano Coll Comp & Informat Sci Rochester NY 14623 USA

ISBN: (纸本)9781728193601

Group activity understanding is a challenging task as multiple people are involved, and their relations may vary over time. Currently, the literature of group activity is limited to group activity recognition, because videos are trimmed in very short duration and focus on a single activity. This slows down the progress in the group activity domain. In this paper, we propose a new large-scale untrimmed compositional group activity dataset RIT-18 based on the volleyball games captured from YouTube. Each clip in our dataset depicts an entire rally which spans the duration from serve to a point being scored. Comprehensive annotations including group activity labels, temporal boundaries of activities, key persons, and winning teams are provided. We describe group activity recognition, future activity anticipation, and rally-level winner prediction challenges, and evaluate several baseline methods over these challenges. We report their performance on our dataset and demonstrate further efforts need to be made. The dataset is available at https://***/actionlab/rit-18.

关键词： computer vision

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Adversarial Light Projection Attacks on Face recognition Systems: A Feasibility Study

Adversarial Light Projection Attacks on Face Recognition Sys...

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

作者： Dinh-Luan Nguyen Arora, Sunpreet S. Wu, Yuhang Yang, Hao Visa Res Palo Alto CA 94306 USA Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781728193601

Deep learning-based systems have been shown to be vulnerable to adversarial attacks in both digital and physical domains. While feasible, digital attacks have limited applicability in attacking deployed systems, including face recognition systems, where an adversary typically has access to the input and not the transmission channel. In such setting, physical attacks that directly provide a malicious input through the input channel pose a bigger threat. We investigate the feasibility of conducting real-time physical attacks on face recognition systems using adversarial light projections. A setup comprising a commercially available web camera and a projector is used to conduct the attack. The adversary uses a transformation-invariant adversarial pattern generation method to generate a digital adversarial pattern using one or more images of the target available to the adversary. The digital adversarial pattern is then projected onto the adversary's face in the physical domain to either impersonate a target (impersonation) or evade recognition (obfuscation). We conduct preliminary experiments using two open-source and one commercial face recognition system on a pool of 50 subjects. Our experimental results demonstrate the vulnerability of face recognition systems to light projection attacks in both white-box and black-box attack settings.

关键词： Deep learning

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NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

NTIRE 2024 Quality Assessment of AI-Generated Content Challe...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Xiaohong Liu Xiongkuo Min Guangtao Zhai Chunyi Li Tengchuan Kou Wei Sun Haoning Wu Yixuan Gao Yuqin Cao Zicheng Zhang Xiele Wu Radu Timofte Fei Peng Huiyuan Fu Anlong Ming Chuanming Wang Huadong Ma Shuai He Zifei Dou Shu Chen Huacong Zhang Haiyi Xie Chengwei Wang Baoying Chen Jishen Zeng Jianquan Yang Weigang Wang Xi Fang Xiaoxin Lv Jun Yan Tianwu Zhi Yabin Zhang Yaohui Li Yang Li Jingwen Xu Jianzhao Liu Yiting Liao Junlin Li Zihao Yu Fengbin Guan Yiting Lu Xin Li Hossein Motamednia S. Farhad Hosseini-Benvidi Ahmad Mahmoudi-Aznaveh Azadeh Mansouri Ganzorig Gankhuyag Kihwan Yoon Yifang Xu Haotian Fan Fangyuan Kong Shiling Zhao Weifeng Dong Haibing Yin Li Zhu Zhiling Wang Bingchen Huang Avinab Saha Sandeep Mishra Shashank Gupta Rajesh Sureddi Oindrila Saha Luigi Celona Simone Bianco Paolo Napoletano Raimondo Schettini Junfeng Yang Jing Fu Wei Zhang Wenzhi Cao Limei Liu Han Peng Weijun Yuan Zhan Li Yihang Cheng Yifan Deng Haohui Li Bowen Qu Yao Li Shuqing Luo Shunzhou Wang Wei Gao Zihao Lu Marcos V. Conde Xinrui Wang Zhibo Chen Ruling Liao Yan Ye Qiulin Wang Bing Li Zhaokun Zhou Miao Geng Rui Chen Xin Tao Xiaoyu Liang Shangkun Sun Xingyuan Ma Jiaze Li Mengduo Yang Haoran Xu Jie Zhou Shiding Zhu Bohan Yu Pengfei Chen Xinrui Xu Jiabin Shen Zhichao Duan Erfan Asadi Jiahe Liu Qi Yan Youran Qu Xiaohui Zeng Lele Wang Renjie Liao

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at cvpr 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact *** video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.

关键词： Image quality computer vision conferences Computational modeling Market research Quality assessment pattern recognition

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Take the Scenic Route: Improving Generalization in vision-and-Language Navigation

Take the Scenic Route: Improving Generalization in Vision-an...

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

作者： Yu, Felix Deng, Zhiwei Narasimhan, Karthik Russakovsky, Olga Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781728193601

In the vision-and-Language Navigation (VLN) task, an agent with egocentric vision navigates to a destination given natural language instructions. The act of manually annotating these instructions is timely and expensive, such that many existing approaches automatically generate additional samples to improve agent performance. However, these approaches still have difficulty generalizing their performance to new environments. In this work, we investigate the popular Room-to-Room (R2R) VLN benchmark and discover that what is important is not only the amount of data you synthesize, but also how you do it. We find that shortest path sampling, which is used by both the R2R benchmark and existing augmentation methods, encode biases in the action space of the agent which we dub as action priors. We then show that these action priors offer one explanation toward the poor generalization of existing works. To mitigate such priors, we propose a path sampling method based on random walks to augment the data. By training with this augmentation strategy, our agent is able to generalize better to unknown environments compared to the baseline, significantly improving model performance in the process.

关键词： computer vision

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Rehearsal-Free Continual Learning over Small Non-IID Batches

Rehearsal-Free Continual Learning over Small Non-IID Batches

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

作者： Lomonaco, Vincenzo Maltoni, Davide Pellegrini, Lorenzo Univ Bologna Bologna Italy

ISBN: (纸本)9781728193601

Robotic vision is a field where continual learning can play a significant role. An embodied agent operating in a complex environment subject to frequent and unpredictable changes is required to learn and adapt continuously. In the context of object recognition, for example, a robot should be able to learn (without forgetting) objects of never before seen classes as well as improving its recognition capabilities as new instances of already known classes are discovered. Ideally, continual learning should be triggered by the availability of short videos of single objects and performed on-line on on-board hardware with fine-grained updates. In this paper, we introduce a novel continual learning protocol based on the CORe50 benchmark and propose two rehearsal-free continual learning techniques, CWR* and AR1*, that can learn effectively even in the challenging case of nearly 400 small non-i.i.d. incremental batches. In particular, our experiments show that AR1* can outperform other state-of-the-art rehearsal-free techniques by more than 15% accuracy in some cases, with a very light and constant computational and memory overhead across training batches.

关键词： Object recognition

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3DQ-Nets: Visual Concepts Emerge in Pose Equivariant 3D Quantized Neural Scene Representations

3DQ-Nets: Visual Concepts Emerge in Pose Equivariant 3D Quan...

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

作者： Prabhudesai, Mihir Lal, Shamit Tung, Hsiao-Yu Fish Harley, Adam W. Potdar, Shubhankar Fragkiadaki, Katerina Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781728193601

Concept learning lies at the very heart of intelligence, providing organizing principles with which to comprehend the world (6). Most computer vision models learn concept classifiers or detectors using labelled examples of object boxes, poses and categories. Self-supervised or unsupervised approaches mostly focus on (pre)training CNNs on auxiliary pretext tasks to lessen the need for human labels for a downstream recognition task. The visual "concepts" learnt from pretext tasks are implicit and represented as distributed neural CNN activations (7). We see the following limitations with representing concepts (solely) as neural activations across multiple layers of a deep network: i) visual memory and computation are not separated (3), which means that computation increases exponentially with the number of visual concepts learnt, ii) concepts are not stored somewhere and cannot be referred to or retrieved on demand, iii) the number of concepts cannot increase automatically based on new visual experiences, rather it is determined by the processing architecture;this misaligns with the idea that animals are capable of spontaneous concept instantiation in novel scenes (2), iv) concepts cannot be mentally manipulated by imagining variations, transformations or mental simulations (8), v) concepts do not have any spatial dimension and are hard to use for spatial reasoning. © 2020 ieee.

关键词： computer vision

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