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

共 52944 条记录，以下是4531-4540 订阅

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LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results

LFNAT 2023 Challenge on Light Field Depth Estimation: Method...

引用

2023 ieee/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Sheng, Hao Liu, Yebin Yu, Jingyi Wu, Gaochang Xiong, Wei Guo, Longzhao Xie, Yanlin Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Wang, Tun Yang, Da Cui, Zhenglong Wang, Sizhe Zhao, Mingyuan Wang, Qiong Chen, Qianyu Liang, Zhengyu Wang, Yingqian Yang, Jungang Yang, Xueting Deng, Junli Cong, Ruixuan Chen, Rongshan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China Tsinghua University China Shanghaitech University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Beijing Meet Yuan Co. Ltd China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada College of Computer Science and Technology Zhejiang University of Technology China National University of Defense Technology China School of Information and Communication Engineering Communication University of China China

ISBN: (纸本)9798350302493

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research. © 2023 ieee.

关键词： Stereo image processing

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Responsible AI in Action: Enhancing Fairness in computer vision through Model Improvement Strategies 3

Responsible AI in Action: Enhancing Fairness in Computer Vis...

引用

3rd International conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Jaiswal, Shruti Sekhar, Shashank Chakraborty, Sushovan Bosch Global Software Technologies Bangalore India

ISBN: (纸本)9798350372120

As facial recognition technology continues to advance, addressing issues of fairness and accuracy in image datasets becomes increasingly critical. This paper outlines a novel approach aimed at simultaneously improving the accuracy of facial recognition systems and promoting fairness within image datasets. Traditional approaches have often struggled to balance these objectives, with accuracy improvements potentially exacerbating biases. In our proposed methodology, we have proposed a single input multi-output model with non-linearity to extract the best feature map to classify age, gender and ethnicity to refine facial recognition models. Our experiments demonstrate that our approach enhances overall accuracy for gender and race and reduces mean absolute error (mae) for age. This research contributes to the ongoing discourse on fairness in AI by providing practical insights by improving the performance of facial recognition systems across diverse demographic groups. We have tested our model on UTKFace dataset and compared its performance with existing CNN models and show cased that to the best of our knowledge, it is achieving least mae for age than state-of-the-art models and almost comparable accuracy for gender and ethnicity without overfitting which is the case for many other existing CNN models. © 2024 ieee.

关键词： computer vision

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Segment Anything in Food Images

Segment Anything in Food Images

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Saeed S. Alahmari Michael Gardner Tawfiq Salem Najran University Saudi Arabia King Faisal University Saudi Arabia Purdue University USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a new approach for food image segmentation utilizing the Segment Anything Model (SAM), with the additional refinement achieved through fine-tuning with Low-Rank Adaptation layers (LoRA). The segmentation task involves generating a binary mask for food in RGB images, with pixels categorized as background or food. We conduct various experiments to assess and compare the performance of our proposed method with previous approaches. Our findings indicate that our method consistently outperforms other techniques, achieving an accuracy of 94.14%. The improved accuracy of our approach highlights its potential for various applications in food image analysis, contributing to the advancement of computer vision techniques in the realm of food recognition and segmentation.

关键词： Image segmentation computer vision Adaptation models Technological innovation Accuracy Image recognition Image analysis

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EFFICIENT MOVING TARGET DETECTION USING RESOURCE-CONSTRAINED NEURAL NETWORKS

EFFICIENT MOVING TARGET DETECTION USING RESOURCE-CONSTRAINED...

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ieee International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Milioris, Dimitris Nokia Bell Labs 12 Rue Jean Bart F-91300 Massy France

ISBN: (纸本)9798350302615

In recent years, the widespread use of autonomous vehicles, such as aerial and automotive, has enhanced our abilities to perform target tracking, dispensing our over-reliance on visual features. With the development of computer vision and deep learning techniques, vision-based classification and recognition have recently received special attention in the scientific community. Moreover, recent advances in the field of neural networks with quantized weights and activations down to single bit precision have allowed the development of models that can be deployed in resource-constrained settings, where a trade-off between task performance and efficiency is accepted. In this work we design an efficient single stage object detector based on CenterNet containing a combination of full precision and binary layers. Our model is easy to train and achieves comparable results with a full precision network trained from scratch while requiring an order of magnitude less FLOP. This opens the possibility of deploying an object detector in applications where time is of the essence and a graphical processing unit (GPU) is absent. We train our model and evaluate its performance by comparing with state-of-the-art techniques, obtaining higher accurate results and provide an insight into the design process of resource constrained neural networks involving trade-offs.

关键词： computer vision object detection resource-constrained neural networks binary activations

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Poster Abstract: Enhancing Human Motion Sensing with synthesized Millimeter-Waves 23

Poster Abstract: Enhancing Human Motion Sensing with synthes...

引用

23rd ACM/ieee International conference on Information Processing in Sensor Networks, IPSN 2024

作者： Zhang, Xiaotong Wang, Kun Li, Zhenjiang Zhang, Jin City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Southern University of Science and Technology Res. Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350362015

This poster introduces SynMotion, a novel mmWave-based human motion sensing system addressing the scarcity of training datasets. By synthesizing mmWave signals using existing vision-based human motion datasets, this system overcomes the challenge of collecting and labeling mmWave data, facilitating wider adoption of mmWave technology for applications like activity recognition, skeleton tracking and radar placement recommendation. © 2024 ieee.

关键词： Millimeter waves

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An Improved EfficientFormerv2 Network for Bone Marrow Cell Classification 5

An Improved EfficientFormerv2 Network for Bone Marrow Cell C...

引用

5th International conference on computer vision, Image and Deep Learning, CVIDL 2024

作者： Gao, Qi Hu, Xueyou Huang, Yinghui Bao, Xi Hefei University School of Advanced Manufacturing Engineering Hefei China Bengbu University School of Computer Science and Information Engineering Bengbu China

ISBN: (纸本)9798350373820

Classification and counting of bone marrow cells is necessary for the diagnosis and treatment of various blood disorders. However, this job needs to be done manually by doctors, which is a long period and high workload. In order to realize the automatic identification of bone marrow blood cells, this paper proposes a model structure based on the improved Efficient Formerv2, which optimizes the token mixer in the model mixing block, and the activation function between convolutions in the feedforward neural network is upgraded from Gelu to Mish. Furthermore, addressing the potential issue of gradient explosion caused by the loss function, the loss function is modified from Cross Entropy to Focal Loss. The proposed model was tested on a public dataset of bone marrow cell images from the Munich Leukemia Laboratory, achieving a maximum classification accuracy of 94.85% in a six-class experiment. This research offers a promising technique for the automatic recognition and classification of bone marrow cell images. © 2024 ieee.

关键词： Image classification

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The Physical Fitness Video Tracking System of Football Players Based on Artificial Intelligence Algorithm

The Physical Fitness Video Tracking System of Football Playe...

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2022 International conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2022

作者： Zhang, Shubing Nanchang Institute of Science and Technology Nanchang China

ISBN: (数字)9781665454575

ISBN: (纸本)9781665454575

Intelligent monitoring technology has become a new research direction in the field of computer vision in recent years. The computer vision system is the video data received from the camera, analyzed and learned through computer vision, and on this basis, the video recognition and visual monitoring, mainly involving pattern recognition and image processing. Today, many researchers, workers and universities in China and abroad are carrying out research on visual inspection and monitoring technology. This paper studies the football player physical fitness video tracking system based on artificial intelligence algorithm, and understands the relevant knowledge theory of the football player video tracking system on the basis of the literature, and then designs the football player video tracking system with artificial intelligence algorithm. The algorithm cited in the design is tested, and the test results show that the recognition rate of the improved algorithm in this paper is above 80%. © 2022 ieee.

关键词： computer vision

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PERF-Net: Pose Empowered RGB-Flow Net 22

PERF-Net: Pose Empowered RGB-Flow Net

引用

22nd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Li, Yinxiao Lu, Zhichao Xiong, Xuehan Huang, Jonathan Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665409155

In recent years, many works in the video action recognition literature have shown that two stream models (combining spatial and temporal input streams) are necessary for achieving state-of-the-art performance. In this paper we show the benefits of including yet another stream based on human pose estimated from each frame-specifically by rendering pose on input RGB frames. At first blush, this additional stream may seem redundant given that human pose is fully determined by RGB pixel values-however we show (perhaps surprisingly) that this simple and flexible addition can provide complementary gains. Using this insight, we propose a new model, which we dub PERF-Net (short for Pose Empowered RGB-Flow Net), which combines this new pose stream with the standard RGB and flow based input streams via distillation techniques and show that our model outperforms the state-of-the-art by a large margin in a number of human action recognition datasets while not requiring flow or pose to be explicitly computed at inference time. The proposed pose stream is also part of the winner solution of the ActivityNet Kinetics Challenge 2020 [1].

关键词： computer vision Computational modeling Streaming media Rendering (computer graphics) Kinetic theory Standards

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A Real-Time Multi-Crops Classification Using Deep Learning Method For Pesticide Spraying in Sustainable Agriculture 2

A Real-Time Multi-Crops Classification Using Deep Learning M...

引用

2nd ieee International conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Nagaraja Hebbar, N. Bhat, Sandeep Mokshashree, M.N. Srinivas University S.I.T AI and DS Research Scholar Mangaluru India S.I.T CSE Mangaluru India S.I.T AI and DS Mangaluru India

ISBN: (纸本)9798350354461

The incorporation of state-of-the-art technologies, such as deep learning algorithms and computer vision, has paved the path for a revolutionary approach to precision agriculture, enabling farmers to reduce the environmental effects of pesticide application while simultaneously increasing crop yields. Researchers can create reliable computer vision systems that can precisely identify and classify various crop varieties in real time by utilizing deep learning techniques. Furthermore, the use of Convolutional Neural Networks (CNNs) in the field of plant image recognition and classification has been investigated, indicating the efficacy of deep learning-based methods in maximizing crop *** suggested system classifies various crop varieties in the field in real time by utilizing deep learning techniques. This entails using massive crop picture datasets to train CNN models so they can recognize different crops accurately under varying settings. With the ability to classify data in real-time, pesticide spraying equipment may be dynamically adjusted to ensure accurate application that is customized to meet the unique requirements of individual crop types. © 2024 ieee.

关键词： CNN computer vision Deep learning Multi-class

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Distilling Knowledge in vision-based Human Motor Assessment for Improved Accuracy and Running Efficiency 10

Distilling Knowledge in Vision-based Human Motor Assessment ...

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10th ieee RAS/EMBS International conference for Biomedical Robotics and Biomechatronics (BioRob)

作者： Zhao, Peijun Shen, Zhan Alencastre-Miranda, Moises Whiteman, David Gervas-Arruga, Javier Krebs, Hermano Igo MIT Dept Mech Engn Cambridge MA 02139 USA Univ Michigan Robot Inst Ann Arbor MI 48109 USA Takeda Dev Ctr Amer Inc Cambridge MA USA

ISBN: (纸本)9798350386523;9798350386530

Numerous neurological disorders can lead to a decline in patients' gross motor function, requiring long-term tracking of the disease progression to tailor treatment plans and evaluate intervention effectiveness. In recent years, researchers have explored computer vision methods as a cost-effective, at-home solution for monitoring human motor control. However, the current state-of-the-art approach relies on transfer learning from a general human action recognition model to address the challenge of limited training dataset size. This results in a relatively hefty model that may strain user's devices when running locally. In this study, we propose distilling knowledge from the teacher model into a lightweight student model to enhance operational efficiency on user's devices. Compared to directly training the student model with a limited amount of data, transferring and distilling knowledge from a generalized model can notably enhance accuracy. The lightweight student network can operate 1.5 to 3 times faster on various user devices compared to the large teacher network, with improvement being more noticeable on older devices in particular. This speed improvement opens up numerous opportunities for the adoption of this technology, facilitating its integration into a wider range of devices and environments.

关键词： Students

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