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Improving metric-based few-shot learning with dynamically scaled softmax loss 期刊论文
IMAGE AND VISION COMPUTING, 2023, 卷号: 140, 页码: 15
作者:  Zhang, Yu;  Zuo, Xin;  Zheng, Xuxu;  Gao, Xiaoyong;  Wang, Bo;  Hu, Weiming
收藏  |  浏览/下载：26/0  |  提交时间：2024/02/22
Few-shot learning  Metric-based learning framework  Softmax loss improvement  
Self-Prior Guided Pixel Adversarial Networks for Blind Image Inpainting 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 10, 页码: 12377-12393
作者:  Wang, Juan;  Yuan, Chunfeng;  Li, Bing;  Deng, Ying;  Hu, Weiming;  Maybank, Stephen
收藏  |  浏览/下载：80/0  |  提交时间：2023/11/16
Blind image inpainting  semantic-discontinuity detection  layout map prediction  pixel generative adversarial network  
Hierarchical Curriculum Learning for No-Reference Image Quality Assessment 期刊论文
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2023, 页码: 20
作者:  Wang, Juan;  Chen, Zewen;  Yuan, Chunfeng;  Li, Bing;  Ma, Wentao;  Hu, Weiming
收藏  |  浏览/下载：68/0  |  提交时间：2023/11/17
No-reference image quality assessment  Hierarchical curriculum learning  Prior knowledge  Cross-dataset quality assessment correlation  
Multi-scale self-attention-based feature enhancement for detection of targets with small image sizes 期刊论文
PATTERN RECOGNITION LETTERS, 2023, 卷号: 166, 页码: 46-52
作者:  Deng, Ying;  Hu, Xingliang;  Li, Bing;  Zhang, Congxuan;  Hu, Weiming
收藏  |  浏览/下载：146/0  |  提交时间：2023/03/20
Detection of targets with small image sizes  Feature enhancement  Multi-scale combination Self-attention  
Cross-Architecture Knowledge Distillation 会议论文
INTERNATIONAL JOURNAL OF COMPUTER VISION, Macau SAR, China, 2022.12.4-2022.12.8
作者:  Yufan Liu;  Jiajiong Cao;  Bing Li;  Weiming Hu;  Jingting Ding;  Liang Li
Adobe PDF(1020Kb)  |  收藏  |  浏览/下载：128/41  |  提交时间：2023/04/23
Knowledge distillation  Cross architecture  Model compression  Deep learning  
Narrowing the Gap: Improved Detector Training With Noisy Location Annotations 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 卷号: 31, 页码: 6369-6380
作者:  Wang, Shaoru;  Gao, Jin;  Li, Bing;  Hu, Weiming
Adobe PDF(1489Kb)  |  收藏  |  浏览/下载：187/23  |  提交时间：2022/11/14
Annotations  Noise measurement  Detectors  Task analysis  Training  Object detection  Degradation  Object detection  noisy label  Bayesian estimation  teacher-student learning  
PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling 期刊论文
IEEE Transactions on Image Processing, 2022, 卷号: 31, 页码: 5121-5133
作者:  Yang, Li;  Xu, Yan;  Wang, Shaoru;  Yuan, Chunfeng;  Zhang, Ziqi;  Li, Bing;  Hu, Weiming
Adobe PDF(3190Kb)  |  收藏  |  浏览/下载：206/32  |  提交时间：2022/09/19
Object detection  prediction decoupling  convolutional neural network  
Web Objectionable Video Recognition Based on Deep Multi-Instance Learning With Representative Prototypes Selection 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2021, 卷号: 31, 期号: 3, 页码: 1222-1233
作者:  Ding, Xinmiao;  Li, Bing;  Li, Yangxi;  Guo, Wen;  Liu, Yao;  Xiong, Weihua;  Hu, Weiming
收藏  |  浏览/下载：238/0  |  提交时间：2021/04/27
Feature extraction  Prototypes  Visualization  Support vector machines  Machine learning  Streaming media  Spectrogram  Representative prototype selection  objectionable video recognition  deep learning  
Multi-Scale Low-Discriminative Feature Reactivation for Weakly Supervised Object Localization 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 页码: 6050-6065
作者:  Wang, Bo;  Yuan, Chunfeng;  Li, Bing;  Ding, Xinmiao;  Li, Zeya;  Wu, Ying;  Hu, Weiming
收藏  |  浏览/下载：180/0  |  提交时间：2021/08/15
Location awareness  Neurons  Task analysis  Pattern recognition  Automation  Search problems  Proposals  Weakly supervised object localization  feature recalibration  multi-scale class activation mapping  
Distractor-aware discrimination learning for online multiple object tracking 期刊论文
PATTERN RECOGNITION, 2020, 期号: 107, 页码: 10
作者:  Zhou, Zongwei;  Luo, Wenhan;  Wang, Qiang;  Xing, Junliang;  Hu, Weiming
Adobe PDF(840Kb)  |  收藏  |  浏览/下载：345/105  |  提交时间：2020/08/31
Multi-object tracking  Distractor-aware discrimination learning  Relational attention learning