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检索条件"机构=Department of Computer Vision and Machine Learning"
73 条 记 录,以下是11-20 订阅
排序:
Better Understanding Differences in Attribution Methods via Systematic Evaluations
arXiv
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arXiv 2023年
作者: Rao, Sukrut Böhle, Moritz Schiele, Bernt The Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken66123 Germany
Deep neural networks are very successful on many vision tasks, but hard to interpret due to their black box nature. To overcome this, various post-hoc attribution methods have been proposed to identify image regions m... 详细信息
来源: 评论
A Comprehensive Analysis of Mamba for 3D Volumetric Medical Image Segmentation
arXiv
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arXiv 2025年
作者: Wang, Chaohan Xie, Yutong Chen, Qi Zhou, Yuyin Wu, Qi Australian Institute for Machine Learning The University of Adelaide Adelaide Australia Computer Vision Department MBZUAI Abu Dhabi United Arab Emirates Computer Science and Engineering Department UC Santa Cruz Santa Cruz United States
Mamba, with its selective State Space Models (SSMs), offers a more computationally efficient solution than Transformers for long-range dependency modeling. However, there is still a debate about its effectiveness in h...
来源: 评论
Underwater Object Detection Enhancement via Channel Stabilization
arXiv
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arXiv 2024年
作者: Ali, Muhammad Khan, Salman Department of Machine Learning Mohamed bin Zayed University of AI Abu Dhabi United Arab Emirates Department of Computer Vision Mohamed bin Zayed University of AI Abu Dhabi United Arab Emirates
The complex marine environment exacerbates the challenges of object detection manifold. With the advent of the modern era, marine trash presents a danger to the aquatic ecosystem, and it has always been challenging to... 详细信息
来源: 评论
UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer Model
arXiv
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arXiv 2025年
作者: Jankovic, Branislava Jangirova, Sabina Ullah, Waseem Khan, Latif U. Guizani, Mohsen Computer Vision Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates
Dangerous surroundings and difficult-to-reach landscapes introduce significant complications for adequate disaster management and recuperation. These problems can be solved by engaging unmanned aerial vehicles (UAVs) ... 详细信息
来源: 评论
SIR-HCL: Semantic-Inconsistency Reasoning and Hybrid Contrastive learning for Efficient Cross-Emotion Anomaly Detection
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IEEE Transactions on Cognitive and Developmental Systems 2025年
作者: Liu, Xin Chen, Qiyan Cheung, Yiu-Ming Peng, Shu-Juan Huaqiao University Department of Computer Science Xiamen361021 China Hong Kong Baptist University Department of Computer Science SAR Hong Kong Hong Kong Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen361021 China Huaqiao University Department of Artificial Intelligence Xiamen China Fujian Province University Key Laboratory of Computer Vision and Machine Learning Huaqiao University Xiamen361021 China
Cross-emotion anomaly detection is an emerging and challenging research topic in cognitive analysis field, which aims at identifying the abnormal emotion pair whose semantic patterns are inconsistent across different ... 详细信息
来源: 评论
Blessemflood21: Advancing Flood Analysis with a High-Resolution Georeferenced Dataset for Humanitarian Aid Support
Blessemflood21: Advancing Flood Analysis with a High-Resolut...
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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)
作者: Vladyslav Polushko Alexander Jenal Jens Bongartz Immanuel Weber Damjan Hatic Ronald Rösch Thomas März Markus Rauhut Andreas Weinmann Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany
Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained t... 详细信息
来源: 评论
Multi-Task learning for Fatigue Detection and Face Recognition of Drivers via Tree-Style Space-Channel Attention Fusion Network
arXiv
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arXiv 2024年
作者: Qu, Shulei Gao, Zhenguo Chen, Xiaowei Li, Na Wang, Yakai Wu, Xiaoxiao Department of Computer Science and Technology Huaqiao University Fujian Xiamen361021 China Key Laboratory of Computer Vision Machine Learning of Fujian Provincial Universities Fujian Xiamen361021 China Department of Mechanical Engineering and Automation Huaqiao University Fujian Xiamen361021 China
In driving scenarios, automobile active safety systems are increasingly incorporating deep learning technology. These systems typically need to handle multiple tasks simultaneously, such as detecting fatigue driving a... 详细信息
来源: 评论
BLESSEMFLOOD21: ADVANCING FLOOD ANALYSIS WITH A HIGH-RESOLUTION GEOREFERENCED DATASET FOR HUMANITARIAN AID SUPPORT
arXiv
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arXiv 2024年
作者: Polushko, Vladyslav Jenal, Alexander Bongartz, Jens Weber, Immanuel Hatic, Damjan Rösch, Ronald März, Thomas Rauhut, Markus Weinmann, Andreas Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Hochschule Darmstadt Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany
Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained t... 详细信息
来源: 评论
B-cos Alignment for Inherently Interpretable CNNs and vision Transformers
arXiv
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arXiv 2023年
作者: Böhle, Moritz Singh, Navdeeppal Fritz, Mario Schiele, Bernt Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Saarbrucken66123 Germany CISPA Helmholtz Center for Information Security Saarbrucken66123 Germany
We present a new direction for increasing the interpretability of deep neural networks (DNNs) by promoting weight-input alignment during training. For this, we propose to replace the linear transformations in DNNs by ... 详细信息
来源: 评论
FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis
arXiv
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arXiv 2025年
作者: Maani, Fadillah Saeed, Numan Saleem, Tausifa Farooq, Zaid Alasmawi, Hussain Diehl, Werner Mohammad, Ameera Waring, Gareth Valappi, Saudabi Bricker, Leanne Yaqub, Mohammad Department of Computer Vision Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates
Foundation models are becoming increasingly effective in the medical domain, offering pre-trained models on large datasets that can be readily adapted for downstream tasks. Despite progress, fetal ultrasound images re... 详细信息
来源: 评论