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检索条件"任意字段=2006 Conference on Computer Vision and Pattern Recognition Workshops"
5506 条 记 录,以下是611-620 订阅
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PEA: Improving the Performance of ReLU Networks for Free by Using Progressive Ensemble Activations
PEA: Improving the Performance of ReLU Networks for Free by ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Utasi, Akos Continental AI Dev Ctr Budapest Hungary
In recent years novel activation functions have been proposed to improve the performance of neural networks, and they show superior performance compared to the ReLU counterpart. However, there are environments, where ... 详细信息
来源: 评论
Unsupervised Anomaly Detection from Time-of-Flight Depth Images
Unsupervised Anomaly Detection from Time-of-Flight Depth Ima...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Schneider, Pascal Rambach, Jason Mirbach, Bruno Stricker, Didier German Res Ctr Artificial Intelligence DFKI Trippstadter Str 122 D-67663 Kaiserslautern Germany
Video anomaly detection (VAD) addresses the problem of automatically finding anomalous events in video data. The primary data modalities on which current VAD systems work on are monochrome or RGB images. Using depth d... 详细信息
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Online Unsupervised Domain Adaptation for Person Re-identification
Online Unsupervised Domain Adaptation for Person Re-identifi...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Rami, Hamza Ospici, Matthieu Lathuiliere, Stephane Inst Polytech Paris Telecom Paris LTCI Paris France Atos London England
Unsupervised domain adaptation for person re-identification (Person Re-ID) is the task of transferring the learned knowledge on the labeled source domain to the unlabeled target domain. Most of the recent papers that ... 详细信息
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Linear Combination Approximation of Feature for Channel Pruning
Linear Combination Approximation of Feature for Channel Prun...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Joo, Donggyu Kim, Doyeon Yi, Eojindl Kim, Junmo Korea Adv Inst Sci & Technol KAIST Daejeon South Korea
Network pruning is an effective method that reduces the computation of neural networks while maintaining high performance. This enables the operation of deep neural networks in resource-limited environments. In a gene... 详细信息
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Momentum Contrastive Pruning
Momentum Contrastive Pruning
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Pan, Siyuan Qin, Yiming Li, Tingyao Li, Xiaoshuang Hou, Liang Shanghai Jiao Tong Univ Shanghai Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China
Momentum contrast [16] (MoCo) for unsupervised visual representation learning has a close performance to supervised learning, but it sometimes possesses excess parameters. Extracting a subnetwork from an over-paramete... 详细信息
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Autonomous detection of disruptions in the intensive care unit using deep mask R-CNN  31
Autonomous detection of disruptions in the intensive care un...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Malhotra, Kumar Rohit Davoudi, Anis Siegel, Scott Bihorac, Azra Rashidi, Parisa Univ Florida Gainesville FL 32611 USA
Patients staying in the Intensive Care Unit (ICU) have a severely disrupted circadian rhythm. Due to patients' critical medical condition, ICU physicians and nurses have to provide round-the-clock clinical care, f... 详细信息
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Unveiling the Anomalies in an Ever-ChangingWorld: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning
Unveiling the Anomalies in an Ever-ChangingWorld: A Benchmar...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Bugarin, Nikola Bugaric, Jovana Barusco, Manuel Pezze, Davide Dalle Susto, Gian Antonio Univ Padua Padua Italy
Anomaly Detection is a relevant problem in numerous real-world applications, especially when dealing with images. However, little attention has been paid to the issue of changes over time in the input data distributio... 详细信息
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The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark
The multi-modal universe of fast-fashion: the Visuelle 2.0 b...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Skenderi, Geri Joppi, Christian Denitto, Matteo Scarpa, Berniero Cristani, Marco Univ Verona Verona Italy Humatics Srl Verona Italy Nuna Lie Srl Rome Italy
We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial i... 详细信息
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Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling
Label-free Anomaly Detection in Aerial Agricultural Images w...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Shikhar, Sambal Sobti, Anupam Plaksha Univ Mohali Punjab India
Detecting various types of stresses (nutritional, water, nitrogen, etc.) in agricultural fields is critical for farmers to ensure maximum productivity. However, stresses show up in different shapes and sizes across di... 详细信息
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DANICE: Domain adaptation without forgetting in neural image compression
DANICE: Domain adaptation without forgetting in neural image...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Katakol, Sudeep Herranz, Luis Yang, Fei Mrak, Marta Univ Michigan Ann Arbor MI 48109 USA UAB Comp Vis Ctr Barcelona Spain BBC R&D London England
Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study t... 详细信息
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