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

共 8962 条记录，以下是1711-1720 订阅

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Multi-Explainable TemporalNet: An Interpretable Multimodal Approach using Temporal Convolutional Network for User-level Depression Detection

Multi-Explainable TemporalNet: An Interpretable Multimodal A...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Anas Zafar Danyal Aftab Rizwan Qureshi Yaofeng Wang Hong Yan Fast School of Computing National University of Computer and Emerging Sciences Karachi Pakistan Technological University Dublin Dublin Ireland Center for Regenerative Medicine and Health Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong SAR China Department of Electrical Engineering City University of Hong Kong Hong Kong

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal depression detection through internet-based data such as social media platforms has been an important problem in the research community, aiming to predict human mental states for ensuring wellbeing of the society. Recently, attention-based networks have gained significant popularity for depression detection. However, existing multimodal methods primarily rely on images and text assuming no correlation between temporal aspects such as relative time of different posts or tweets, which is a crucial factor in deriving depression related behavior patterns. Moreover, they lack model interpretability resulting in limited understanding of how different features are contributing to the model’s final prediction. In this paper, we propose MultiExplainable TemporalNet (METN), a Temporal Convolution Network (TCN) based multi-modal transformer network with relative timestamp embeddings. We leverage pretrained foundation models for text and image embeddings and attention maps for model interpretability. We perform extensive experiments and ablation studies to validate the performance of METN for user-level depression detection task. Our model shows state-of-the-art results on various benchmarks, such as 0.945 F1 score on multimodal Twitter dataset, and 0.913 F1 score on multimodal Reddit dataset. We further demonstrate that our model enhances the accuracy of identifying depression in individuals who publicly post messages on social media platforms with enhanced interpretable compatibility. Code and models are available at Github. 1

关键词： Training Attention mechanisms Accuracy Social networking (online) Computational modeling Blogs Predictive models

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Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report

Efficient Deep Models for Real-Time 4K Image Super-Resolutio...

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

作者： Conde, Marcos V. Zamfir, Eduard Timofte, Radu Motilla, Daniel Liu, Cen Zhang, Zexin Peng, Yunbo Lin, Yue Guo, Jiaming Zou, Xueyi Chen, Yuyi Liu, Yi Hao, Jia Yan, Youliang Zhang, Yuanfan Li, Gen Sun, Lei Kong, Lingshun Bai, Haoran Pan, Jinshan Dong, Jiangxin Tang, Jinhui Ayazoglu, Mustafa Bilecen, Bahri Batuhan Li, Mingxi Zhang, Yuhang Fan, Xianjun Sheng, Yankai Sun, Long Liu, Zibin Gou, Weiran Li, Shaoqing Yi, Ziyao Xiang, Yan Kong, Dehui Xu, Ke Gankhuyag, Ganzorig Yoon, Kihwan Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Zhou, Zhou Chao, Jiahao Gao, Hongfan Gong, Jiali Yang, Zhengfeng Zeng, Zhenbing Chen, Chengpeng Guo, Zichao Park, Anjin Liu, Yuqing Jia, Qi Yu, Hongyuan Yin, Xuanwu Zuo, Kunlong Zhang, Dongyang Fu, Ting Cheng, Zhengxue Zhu, Shiai Zhou, Dajiang Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Shao, Ben Zheng, Shaolong Yin, Daheng Chen, Baijun Liu, Mengyang Nistor, Marian-Sergiu Chen, Yi-Chung Huang, Zhi-Kai Chiang, Yuan-Chun Chen, Wei-Ting Yang, Hao-Hsiang Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Vo, Tu Yan, Qingsen Zhu, Yun Su, Jinqiu Zhang, Yanning Zhang, Cheng Luo, Jiaying Cho, Youngsun Lee, Nakyung Computer Vision Lab CAIDAS IFI University of Würzburg Germany Sony Interactive Entertainment CA United States Huawei Technologies Co. Ltd. China NetEase Games AI Lab Nanjing University of Science and Technology China Tencent China Attrsense Korea Republic of Sanechips Co Ltd Ant Group China East China Normal University China Shopee Dalian University of Technology Xiaomi Inc. China China Zhejiang Dahua Technology Co. Ltd. China Multimedia Department Xiaomi Inc. China Korea Photonic Technology Institute Korea Republic of School of Computer Science and Engineering Southeast University China University Al. I. Cuza Iasi Romania Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Northwestern Polytechnical University China KC Machine Learning Lab CJ OliveNetworks AI Research

ISBN: (纸本)9798350302493

This paper introduces a novel benchmark for efficient up-scaling as part of the NTIRE 2023 Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images from 720p and 1080p resolution to native 4K (×2 and ×3 factors) in real-time on commercial GPUs. For this, we use a new test set containing diverse 4K images ranging from digital art to gaming and photography. We assessed the methods devised for 4K SR by measuring their runtime, parameters, and FLOPs, while ensuring a minimum PSNR fidelity over Bicubic interpolation. Out of the 170 participants, 25 teams contributed to this report, making it the most comprehensive benchmark to date and showcasing the latest advancements in real-time SR. © 2023 ieee.

关键词： Program processors

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DOAD: Decoupled One Stage Action Detection Network

DOAD: Decoupled One Stage Action Detection Network

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Shuning Chang Pichao Wang Fan Wang Jiashi Feng Mike Zheng Shou Showlab National University of Singapore Alibaba Group National University of Singapore

ISBN: (数字)9798350302493

ISBN: (纸本)9798350302509

Localizing people and recognizing their actions from videos is a challenging task towards high-level video understanding. Existing methods are mostly two-stage based, with one stage for person bounding box generation and the other stage for action recognition. However, such two-stage methods are generally with low efficiency. We observe that directly unifying detection and action recognition normally suffers from (i) inferior learning due to different desired properties of context representation for detection and action recognition; (ii) optimization difficulty with insufficient training data. In this work, we present a decoupled onestage network dubbed DOAD, to mitigate above issues and improve the efficiency for spatio-temporal action detection. To achieve it, we decouple detection and action recognition into two branches. Specifically, one branch focuses on detection representation for actor detection, and the other one for action recognition. For the action branch, we design a transformer-based module (TransPC) to model pairwise relationships between people and context. Different from commonly used vector-based dot product in self-attention, it is built upon a novel matrix-based key and value for Hadamard attention to model person-context information. It not only exploits relationships between person pairs but also takes into account context and relative position information. The results on AVA and UCF101-24 datasets show that our method is competitive with two-stage state-of-theart methods with significant efficiency improvement.

关键词： computer vision conferences Training data Transformers Feature extraction pattern recognition Task analysis

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NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

NTIRE 2024 Challenge on Short-form UGC Video Quality Assessm...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xin Li Kun Yuan Yajing Pei Yiting Lu Ming Sun Chao Zhou Zhibo Chen Radu Timofte Wei Sun Haoning Wu Zicheng Zhang Jun Jia Zhichao Zhang Linhan Cao Qiubo Chen Xiongkuo Min Weisi Lin Guangtao Zhai JianHui Sun Tianyi Wang Lei Li Han Kong Wenxuan Wang Bing Li Cheng Luo Haiqiang Wang Xiangguang Chen Wenhui Meng Xiang Pan Huiying Shi Han Zhu Xiaozhong Xu Lei Sun Zhenzhong Chen Shan Liu Fangyuan Kong Haotian Fan Yifang Xu Haoran Xu Mengduo Yang Jie Zhou Jiaze Li Shijie Wen Mai Xu Da Li Shunyu Yao Jiazhi Du Wangmeng Zuo Zhibo Li Shuai He Anlong Ming Huiyuan Fu Huadong Ma Yong Wu Fie Xue Guozhi Zhao Lina Du Jie Guo Yu Zhang Huimin Zheng Junhao Chen Yue Liu Dulan Zhou Kele Xu Qisheng Xu Tao Sun Zhixiang Ding Yuhang Hu

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://***/lixinustc/KVQ-Challenge-CVPR-NTIRE2024.

关键词： Training computer vision Reviews Databases conferences Benchmark testing Quality assessment

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LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction

LGFN: Lightweight Light Field Image Super-Resolution using L...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zhongxin Yu Liang Chen Zhiyun Zeng Kunping Yang Shaofei Luo Shaorui Chen Cheng Zhong Fujian Normal University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Capturing different intensity and directions of light rays at the same scene, Light field (LF) can encode the 3D scene cues into a 4D LF image, which has a wide range of applications (i.e., post-capture refocusing and depth sensing). LF image super-resolution (SR) aims to improve the image resolution limited by the performance of LF camera sensor. Although existing methods have achieved promising results, the practical application of these models is limited because they are not lightweight enough. In this paper, we propose a lightweight model named LGFN, which integrates the local and global features of different views and the features of different channels for LF image SR. Specifically, owing to neighboring regions of the same pixel position in different sub-aperture images exhibit similar structural relationships, we design a lightweight CNN-based feature extraction module (namely, DGCE) to extract local features better through feature modulation. Meanwhile, as the position beyond the boundaries in the LF image presents a large disparity, we propose an efficient spatial attention module (namely, ESAM) which uses decomposable large-kernel convolution to obtain an enlarged receptive field and an efficient channel attention module (namely, ECAM). Compared with the existing LF image SR models with large parameter, our model has a parameter of 0.45M and a FLOPs of 19.33G, which has achieved a competitive effect. Extensive experiments with ablation studies demonstrate the effectiveness of our proposed method, which ranked the second place in the Track 2 Fidelity & Efficiency of NTIRE2024 Light Field Super Resolution Challenge and the seventh place in the Track 1 Fidelity.

关键词： computer vision Three-dimensional displays Convolution conferences Superresolution Modulation Feature extraction

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EfficientNet-SAM: A Novel EffecientNet with Spatial Attention Mechanism for COVID-19 Detection in Pulmonary CT Scans

EfficientNet-SAM: A Novel EffecientNet with Spatial Attentio...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ramy Farag Parth Upadhay Jacket Demby’s Yixiang Gao Katherin Garces Montoya Seyed Mohamad Ali Tousi Gbenga Omotara Guilherme DeSouza Department of Electrical Engineering and Computer Science Vision-Guided and Intelligent Robotics Lab - ViGIR Lab University of Missouri-Columbia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors, especially given high volume of patients and numerous images per patient. So, we address the need for automation of this task by developing a new deep learning-based pipeline. Our motivation was sparked by the CVPR Workshop on "Domain Adaptation, Explainability and Fairness in AI for Medical Image Analysis", more specifically, the "COVID-19 Diagnosis Competition (DEF-AI-MIA COV19D)" under the same Workshop. This challenge provides an opportunity to assess our proposed pipeline for COVID-19 detection from CT scan images. The same pipeline incorporates one of the architectures in the EfficientNet "family", but with an added Spatial Attention Mechanism: EfficientNet-SAM. Also, unlike the traditional/past pipelines, which relied on a preprocessing step, our pipeline takes the raw selected input images without any such step, except for an image-selection step to simply reduce the number of CT images required for training and/or testing. Moreover, our pipeline is computationally efficient, as, for example, it does not incorporate a decoder for segmenting the lungs. It also does not combine different models nor combine RNN with a backbone, as other pipelines in the past did. Nevertheless, our pipeline outperformed all approaches presented by other teams in last year’s instance of the same challenge using the validation subset. It also placed 5th in this year’s competition, ranking less than 1.3% below the 1st place and close to 3.5% above the 6th place based on the macro-F1 score.

关键词： COVID-19 Training Visualization Attention mechanisms Computed tomography conferences Pipelines

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Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise

Manifold DivideMix: A Semi-Supervised Contrastive Learning F...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Fahimeh Fooladgar Minh Nguyen Nhat To Parvin Mousavi Purang Abolmaesumi Department of Electrical and Computer Engineering University of British Columbia CA School of Computing Queen’s University CA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep neural networks have proven to be highly effective when large amounts of data with clean labels are available. However, their performance degrades when training data contains noisy labels, leading to poor generalization on the test set. Real-world datasets contain noisy label samples that either have similar visual semantics to other classes (in-distribution) or have no semantic relevance to any class (out-of-distribution) in the dataset. Most state-of-the-art methods leverage ID labeled noisy samples as unlabeled data for semi-supervised learning, but OOD labeled noisy samples cannot be used in this way because they do not belong to any class within the dataset. Hence, in this paper, we propose incorporating the information from all the training data by leveraging the benefits of self-supervised training. Our method aims to extract a meaningful and generalizable embedding space for each sample regardless of its label. Then, we employ a simple yet effective K-nearest neighbor method to remove portions of out-of-distribution samples. By discarding these samples, we propose an iterative "Manifold DivideMix" algorithm to find clean and noisy samples, and train our model in a semi-supervised way. In addition, we propose "MixEMatch", a new algorithm for the semi-supervised step that involves mixup augmentation at the input and final hidden representations of the model. This will extract better representations by interpolating both in the input and manifold spaces. Extensive experiments on multiple synthetic-noise image benchmarks and real-world web-crawled datasets demonstrate the effectiveness of our proposed framework. Code is available at https://***/Fahim-F/ManifoldDivideMix.

关键词： Manifolds Representation learning Noise Semantics Training data Clustering algorithms Contrastive learning

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DepthVoting: A Few-Shot Point Cloud Classification Model Incorporating a Projection-Based Voting Mechanism

DepthVoting: A Few-Shot Point Cloud Classification Model Inc...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yunhui Zhu Jiajing Chen Senem Velipasalar Electrical Engineering and Computer Science Dept. Syracuse University Syracuse NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite the significant progress in few-shot 2D image classification, few-shot 3D point cloud classification remains relatively under-explored, particularly in addressing the challenges posed by missing points in 3D point clouds. Most existing methods for few-shot 3D point cloud classification are point-based, and thus, highly sensitive to missing points. Despite recent attempts, such as ViewNet, which introduce projection-based backbones to increase robustness against missing points, the reliance on max pooling, to extract information from multiple images simultaneously, makes them prone to information loss. To address these limitations, we introduce DepthVoting, a novel projection-based approach, for few-shot 3D point cloud classification. Instead of extracting features from multiple projection images simultaneously, DepthVoting captures features from pairs of projection images (obtained from opposite view angles) separately, enhancing the extraction of more comprehensive information. These features are sent to multiple few-shot heads, which share parameters. To further refine predictions, DepthVoting incorporates a voting mechanism, allowing contribution and incorporating information from different pairs. We conduct extensive experiments on three datasets, namely ModelNet40, ModelNet40-C, and ScanObjectNN, along with cross-validation. Our proposed method consistently outperforms the state-of-the-art baselines on all datasets in terms of average accuracy with even higher margins on the challenging ScanObjectNN dataset.

关键词： Point cloud compression Solid modeling Three-dimensional displays Head Accuracy Feature extraction Data augmentation

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Development of a Verilog-Based System for pattern Detection Using Convolutional Neural Networks 2

Development of a Verilog-Based System for Pattern Detection ...

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2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Kandula, Bala Sindhuri Singh, Sangeeta Tata, Subhashini Manasaveena, T. Bolisetty, V Vijayasri Sadguna Kumari, V. S.R.K.R Engineering College Department of E.C.E Bhimavaram India Vardhaman College of Engineering Hyderabad Department of E.C.E Telangana India SR Gudlavalleru Engineering College Department of E.C.E Gudlavalleru India NRI Institute of Technology Department of E.C.E Vijayawada India Aditya College of Engineering and Technology Department of E.C.E Surampalem India Pydah College of Engineering Department of E.C.E Kakinada India

ISBN: (纸本)9798350366570

pattern detection is a foundational element in computer vision, crucial for advancements in autonomous driving, surveillance, and numerous other fields. Convolutional Neural Networks (CNNs) have significantly enhanced pattern detection by improving both accuracy and efficiency. This paper aims to develop a Verilog-based system for pattern detection using CNNs, focusing on detecting and counting occurrences of a specified test pattern within input images. The CNN architecture employs Convolutional Layers for feature extraction and Max Pooling Layers for spatial downsampling, both essential in image processing. Through iterative weight adjustments during training, the CNN learns to recognize patterns similar to the test pattern. After training, the system evaluates unseen images, predicting the presence and count of test pattern instances. To optimize performance, the system incorporates approximate adders to reduce area and gate count, thereby enhancing efficiency. These optimizations enable the system to achieve high performance while maintaining computational efficiency, providing detection and counting of specified patterns in images. This capability offers valuable insights and practical solutions for various real-world applications. © 2024 ieee.

关键词： Convolutional neural networks

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A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception

A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency ...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Asude Aydin Mathias Gehrig Daniel Gehrig Davide Scaramuzza Robotics and Perception Group University of Zurich Switzerland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Spiking Neural Networks (SNNs) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge-devices through the use of asynchronous and sparse processing. However, being temporal models, SNNs depend heavily on expressive states to generate predictions on par with classical artificial neural networks (ANNs). These states converge only after long transient time periods, and quickly decay in the absence of input data, leading to higher latency, power consumption, and lower accuracy. In this work, we address this issue by initializing the state with an auxiliary ANN running at a low rate. The SNN then uses the state to generate predictions with high temporal resolution until the next initialization phase. Our hybrid ANN-SNN model thus combines the best of both worlds: It does not suffer from long state transients and state decay thanks to the ANN, and can generate predictions with high temporal resolution, low latency, and low power thanks to the SNN. We show for the task of event-based 2D and 3D human pose estimation that our method consumes 88% less power with only a 4% decrease in performance compared to its fully ANN counterparts when run at the same inference rate. Moreover, when compared to SNNs, our method achieves a 74% lower error. This research thus provides a new understanding of how ANNs and SNNs can be used to maximize their respective benefits. Code: https://***/uzh-rpg/hybrid_ann_snn

关键词： Accuracy Three-dimensional displays Pose estimation computer architecture Spiking neural networks Predictive models Low latency communication

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