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Diff-pcg: diffusion point cloud generation conditioned on continuous normalizing flow 期刊论文
The Visual Computer, 2024, 页码: 1-15
作者:  Yu T(余挺);  Meng WL(孟维亮);  Wu ZQ(吴仲琦);  Guo JW(郭建伟);  Zhang XP(张晓鹏)
Adobe PDF(2471Kb)  |  收藏  |  浏览/下载：29/7  |  提交时间：2024/06/11
3D shape generation  Diffusion model  Continuous normalizing flow  Point cloud  
SocialVis: Dynamic Social Visualization in Dense Scenes via Real-time Multi-Object Tracking and Proximity Graph Construction 期刊论文
Computer Animation and Virtual Worlds, 2024, 卷号: 35, 期号: 3, 页码: 1-15
作者:  Li BW(李博文);  Li W(李巍);  Wang JQ(王镜淇);  Meng WL(孟维亮);  Zhang JG(张吉光);  Zhang XP(张晓鹏)
Adobe PDF(2914Kb)  |  收藏  |  浏览/下载：33/5  |  提交时间：2024/06/04
dense pedestrian  detection  multi-object tracking  proximity graph  visualization  
Multi-Correlation Siamese Transformer Network With Dense Connection for 3D Single Object Tracking 期刊论文
IEEE ROBOTICS AND AUTOMATION LETTERS, 2023, 卷号: 8, 期号: 12, 页码: 8066-8073
作者:  Feng, Shihao;  Liang, Pengpeng;  Gao, Jin;  Cheng, Erkang
Adobe PDF(2745Kb)  |  收藏  |  浏览/下载：122/6  |  提交时间：2023/12/21
3D object tracking  Point cloud  Transformer  
Reducing Vision-Answer Biases for Multiple-Choice VQA 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2023, 卷号: 32, 页码: 4621-4634
作者:  Zhang, Xi;  Zhang, Feifei;  Xu, Changsheng
Adobe PDF(2684Kb)  |  收藏  |  浏览/下载：85/2  |  提交时间：2023/11/17
Multiple-choice VQA  vision-answer bias  causal intervention  counterfactual interaction learning  
Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2023, 卷号: 33, 期号: 9, 页码: 4616-4629
作者:  Ma, Chengcheng;  Liu, Yang;  Deng, Jiankang;  Xie, Lingxi;  Dong, Weiming;  Xu, Changsheng
Adobe PDF(1644Kb)  |  收藏  |  浏览/下载：153/21  |  提交时间：2023/11/16
Vision-language model  prompt tuning  over-fitting  subspace learning  gradient projection  
Weakly-Supervised Video Object Grounding Via Learning Uni-Modal Associations 期刊论文
IEEE Transactions on Multimedia, 2022, 卷号: 25, 页码: 1-12
作者:  Wang, Wei;  Gao, Junyu;  Xu, Changsheng
Adobe PDF(5406Kb)  |  收藏  |  浏览/下载：133/39  |  提交时间：2023/04/25
Visualization  Grounding  Task analysis  Prototypes  Annotations  Uncertainty  Proposals  Cross-modal retrieval  weakly-supervised learning  video object grounding  uni-modal association  
Rethinking Pretraining as a Bridge From ANNs to SNNs 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 14
作者:  Lin, Yihan;  Hu, Yifan;  Ma, Shijie;  Yu, Dongjie;  Li, Guoqi
收藏  |  浏览/下载：325/0  |  提交时间：2023/03/20
Training  Task analysis  Neurons  Pipelines  Artificial neural networks  Feature extraction  Transfer learning  Event-driven dataset  neural network (NN) analysis  pretraining technique  spiking NN (SNN)  transfer learning  
A Collision-Free Planning and Control Framework for a Biomimetic Underwater Vehicle in Dynamic Environments 期刊论文
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2023, 卷号: 28, 期号: 3, 页码: 1415 - 1424
作者:  Lv, Jiaqi;  Wang, Yu;  Wang, Shuo;  Bai, Xuejian;  Wang, Rui;  Tan, Min
Adobe PDF(3810Kb)  |  收藏  |  浏览/下载：379/79  |  提交时间：2023/02/22
Arctangent nonsingularity terminal sliding mode (ANTSM) control  biomimetic underwater vehicle (BUV)  collision-free in dynamic environments  fuzzy artificial potential field (FAPF) planning  robots  
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：274/34  |  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：319/41  |  提交时间：2022/09/19
Object detection  prediction decoupling  convolutional neural network