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

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LWTSEO-VT: Lightweight TwinScope Election Optimizer based-vision Transformer for Autism Spectral Disorder Detection for Toddlers and Adults 2

LWTSEO-VT: Lightweight TwinScope Election Optimizer based-Vi...

引用

2nd ieee International conference on Emerging Research in Computational Science, ICERCS 2024

作者： Balasubramani, Jagadesh Surendran, R. Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India Saveetha School of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798331534950

autism spectrum disorder (ASD) is a neurological condition that disturbs an individual's capability to attach and communicate with others. It instigates in childhood and continues beyond adolescence and adulthood. Because of complicated procedures and limitations of existing methods, it has been difficult to diagnose. One unsolved problem is determining how to learn discriminative deep representations for building computational models. In this paper, a new Lightweight TwinScope Election Optimizer based-vision Transformer (LWTSEO-VT) for ASD recognition is proposed for toddlers and adults. First, pre-processing is applied to improve the data quality and then extracted the features using Path-selective LASSO regression model. Lastly, a Lightweight TwinScope vision Transformer (LWTS-VT) based on election optimizer is used for detecting autism. The LWTSEO-VT model implemented using python platform using autism screening data for toddlers and autism screening data for adults datasets. The efficacy and dependability of the LWTSEO-VT model have been demonstrated by quantitative comparisons © 2024 ieee.

关键词： Diseases

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Few-Shot Open-Set recognition of Hyperspectral Images with Outlier Calibration Network 22

Few-Shot Open-Set Recognition of Hyperspectral Images with O...

引用

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

作者： Pal, Debabrata Bundele, Valay Sharma, Renuka Banerjee, Biplab Jeppu, Yogananda Indian Inst Technol Bombay Maharashtra India Honeywell Technol Solut Bengaluru India

ISBN: (纸本)9781665409155

We tackle the few-shot open-set recognition (FSOSR) problem in the context of remote sensing hyperspectral image (HSI) classification. Prior research on OSR mainly considers an empirical threshold on the class prediction scores to reject the outlier samples. Further, recent endeavors in few-shot HSI classification fail to recognize outliers due to the `closed-set' nature of the problem and the fact that the entire class distributions are unknown during training. To this end, we propose to optimize a novel outlier calibration network (OCN) together with a feature extraction module during the meta-training phase. The feature extractor is equipped with a novel residual 3D convolutional block attention network (R3CBAM) for enhanced spectral-spatial feature learning from HSI. Our method rejects the outliers based on OCN prediction scores barring the need for manual thresholding. Finally, we propose to augment the query set with synthesized support set features during the similarity learning stage in order to combat the data scarcity issue of few-shot learning. The superiority of the proposed model is showcased on four benchmark HSI datasets.(1)

关键词： Training Representation learning computer vision Image recognition Three-dimensional displays Manuals Benchmark testing

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Eye State Detection Based on MTCNN and ESP

Eye State Detection Based on MTCNN and ESP

引用

2023 ieee International conference on Sensors, Electronics and computer Engineering, ICSECE 2023

作者： Yang, Wenhao Yuan, Haojun Li, Pengcheng Computer School of South China Hengyang China

ISBN: (纸本)9798350313734

Eye state detection is an essential task in computer vision with diverse applications including emotion recognition, fatigue detection in high-risk areas, and computer interaction. This paper introduces a classification model called ERNet, which leverages the Efficient Spatial Pyramid (ESP) technique. The ERNet model is combined with the MTCNN to create a novel eye state detection model called MTED. The ERNet proposed in this paper has better performance than the existing model on the MRL Eyes 2018 dataset. Our ERNet model is more than 99.06% accurate on the MRL Eyes 2018 dataset while maintaining a high processing speed of 87 frames per second. Furthermore, the MTED model achieves a commendable accuracy of 95.41% on the ABD dataset. © 2023 ieee.

关键词： Emotion recognition

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UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning

UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Lea...

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

作者： Luo, Kunming Wang, Chuan Liu, Shuaicheng Fan, Haoqiang Wang, Jue Sun, Jian Megvii Technol Beijing Peoples R China Univ Elect Sci & Technol China Beijing Peoples R China

ISBN: (纸本)9781665445092

We present an unsupervised learning approach for optical flow estimation by improving the upsampling and learning of pyramid network. We design a self-guided upsample module to tackle the interpolation blur problem caused by bilinear upsampling between pyramid levels. Moreover, we propose a pyramid distillation loss to add supervision for intermediate levels via distilling the finest flow as pseudo labels. By integrating these two components together, our method achieves the best performance for unsupervised optical flow learning on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015. In particular, we achieve EPE=1.4 on KITTI 2012 and F1=9.38% on KITTI 2015, which outperform the previous state-of-the-art methods by 22.2% and 15.7%, respectively.

关键词： Optical losses Interpolation computer vision Estimation Benchmark testing pattern recognition Optical flow

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Generate Vector Graphics of Fine-grained pattern based on the Xception Edge Detection 4

Generate Vector Graphics of Fine-grained Pattern based on th...

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4th International conference on computer vision, Application, and Algorithm, CVAA 2024

作者： Chen, Anqi Chen, Hao Peng, Yicui Hu, Jinrong Deng, Linjie Wen, Xiang Wang, Xin Chengdu Technological University Chengdu China Chengdu University of Information Technology Chengdu China Industry Research Institute Chengdu China University at Buffalo United States

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

The innovative generation of vector graphics with fine-grained images using Artificial Intelligence has become an important task in edge extraction. In this paper, we take Qiang embroidery image as an example due to its containing fine-grained edges, which is more suitable for the study of image processing and pattern recognition. We firstly adopt appropriate pre-processing methods, improved adaptive median filtering (IAMF) for the image to reduce image noise. Then, the Xception based on convolutional neural networks is used for edge detection and extraction. Results show that Qiang embroidery images, after denoising and edge extraction, can be clearly identified the shape characteristics of the images. Based on this approach, it can be converted into vector graphics for digital preservation and further artistic reinterpretation. The use of the Xception effectively addresses Qiang embroidery extraction in two-dimensional vector images, offering a practical reference for preserving related intangible cultural heritage. © 2025 SPIE.

关键词： Median filters

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Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search

Landmark Regularization: Ranking Guided Super-Net Training i...

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

作者： Yu, Kaicheng Ranftl, Rene Salzmann, Mathieu Ecole Polytech Fed Lausanne CVLab Lausanne Switzerland Intel Intelligent Syst Lab Santa Clara CA USA

ISBN: (纸本)9781665445092

Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware. However, recent works have empirically shown a ranking disorder between the performance of stand-alone architectures and that of the corresponding shared-weight networks. This violates the main assumption of weight-sharing NAS algorithms, thus limiting their effectiveness. We tackle this issue by proposing a regularization term that aims to maximize the correlation between the performance rankings of the shared-weight network and that of the standalone architectures using a small set of landmark architectures. We incorporate our regularization term into three different NAS algorithms and show that it consistently improves performance across algorithms, search-spaces, and tasks.

关键词： Training computer vision Correlation Limiting Computational modeling computer architecture Hardware

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NUTA: Non-uniform Temporal Aggregation for Action recognition 22

NUTA: Non-uniform Temporal Aggregation for Action Recognitio...

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

作者： Li, Xinyu Liu, Chunhui Shuai, Bing Zhu, Yi Chen, Hao Tighe, Joseph Amazon Web Serv Seattle WA 98109 USA

ISBN: (纸本)9781665409155

In the world of action recognition research, one primary focus has been on how to construct and train networks to model the spatial-temporal volume of an input video. These methods typically uniformly sample a segment of an input clip (along the temporal dimension). However, not all parts of a video are equally important to determine the action in the clip. In this work, we focus instead on learning where to extract features, so as to focus on the most informative parts of the video. We propose a method called the non-uniform temporal aggregation (NUTA), which aggregates features only from informative temporal segments. We also introduce a synchronization method that allows our NUTA features to be temporally aligned with traditional uniformly sampled video features, so that both local and clip-level features can be combined. Our model has achieved state-of-the-art performance on four widely used large-scale action-recognition datasets (Kinetics400, Kinetics700, Something-something V2 and Charades). In addition, we have created a visualization to illustrate how the proposed NUTA method selects only the most relevant parts of a video clip.

关键词： Visualization Solid modeling computer vision Aggregates Feature extraction Synchronization Task analysis

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Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning

Exponential Moving Average Normalization for Self-supervised...

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

作者： Cai, Zhaowei Ravichandran, Avinash Maji, Subhransu Fowlkes, Charless Tu, Zhuowen Soatto, Stefano Amazon Web Serv Seattle WA 98109 USA

ISBN: (纸本)9781665445092

We present a plug-in replacement for batch normalization (BN) called exponential moving average normalization (EMAN), which improves the performance of existing student-teacher based self- and semi-supervised learning techniques. Unlike the standard BN, where the statistics are computed within each batch, EMAN, used in the teacher, updates its statistics by exponential moving average from the BN statistics of the student. This design reduces the intrinsic cross-sample dependency of BN and enhances the generalization of the teacher. EMAN improves strong baselines for self-supervised learning by 4-6/1-2 points and semi-supervised learning by about 7/2 points, when 1%/10% supervised labels are available on ImageNet. These improvements are consistent across methods, network architectures, training duration, and datasets, demonstrating the general effectiveness of this technique. The code will be made available online.

关键词： Training computer vision Codes Semisupervised learning Network architecture pattern recognition Standards

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UAM-Net: An Attention-Based Multi-level Feature Fusion UNet for Remote Sensing Image Segmentation 6th

UAM-Net: An Attention-Based Multi-level Feature Fusion UNet ...

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6th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Cao, Yiwen Jiang, Nanfeng Wang, Da-Han Wu, Yun Zhu, Shunzhi Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understan Xiamen 361024 Peoples R China

ISBN: (纸本)9789819984619;9789819984626

Semantic segmentation of Remote Sensing Images (RSIs) is an essential application for precision agriculture, environmental protection, and economic assessment. While UNet-based networks have made significant progress, they still face challenges in capturing long-range dependencies and preserving fine-grained details. To address these limitations and improve segmentation accuracy, we propose an effective method, namely UAM-Net (UNet with Attention-based Multi-level feature fusion), to enhance global contextual understanding and maintain fine-grained information. To be specific, UAM-Net incorporates three key modules. Firstly, the Global Context Guidance Module (GCGM) integrates semantic information from the Pyramid Pooling Module (PPM) into each decoder stage. Secondly, the Triple Attention Module (TAM) effectively addresses feature discrepancies between the encoder and decoder. Finally, the computation-effective Linear Attention Module (LAM) seamlessly fuses coarse-level feature maps with multiple decoder stages. With the corporations of these modules, UAM-Net significantly outperforms the most state-of-the-art methods on two popular benchmarks.

关键词： Semantic segmentation U-shape architecture Attention mechanism Feature fusion Remote sensing images

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Food Image recognition and Volume Estimation: A Comprehensive Study for Dietary Assessment 9

Food Image Recognition and Volume Estimation: A Comprehensiv...

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9th ieee International conference for Convergence in Technology, I2CT 2024

作者： Jain, Druwil Uphade, Aditi Ugale, Sakshi Kumbhar, Soham Wangikar, Vaishali C. Mit Academy of Engineering School of Computer Engineering Alandi Dist. Pune India

ISBN: (纸本)9798350394474

The paper discusses the importance of health and wellness in today's tech-dependent society and introduces the model named as "Food Diet Recaller App - FDRA". This app employs AI and computer vision to help users track and manage their diets effectively. It features food recognition, quantity estimation, and provides detailed nutritional information. The research utilizes techniques like image recognition and volume estimation, to accurately measure food intake. It also incorporates the InceptionV3 model for image classification an impressive accuracy of approximately 92% is achieved. The Ninja API offers nutritional information for classified food items. With the reference of aruco marker users capture images of their food, and the app calculates volume and density to determine nutritional content. The food volume estimation model is specifically trained for linear-shaped food items, characterized by square or rectangular surfaces. Additionally, the development of a dataset and regression models is mentioned to improve accuracy in estimating food mass. The paper also highlights the popularity of diet recall apps and their role in helping users maintain balanced nutrition. © 2024 ieee.

关键词： Nutrition

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