105 citations · 154 across the 11 of their papers we have counts for
10 papers · 1 filter
Foster Adaptivity and Balance in Learning with Noisy Labels
Mengmeng Sheng, Zeren Sun, Tao Chen +3
Label noise is ubiquitous in real-world scenarios, posing a practical challenge to supervised models due to its effect in hurting the generalization performance of deep neural netw…
Knowledge Transfer with Simulated Inter-Image Erasing for Weakly Supervised Semantic Segmentation
Tao Chen, XiRuo Jiang, Gensheng Pei +3
Though adversarial erasing has prevailed in weakly supervised semantic segmentation to help activate integral object regions, existing approaches still suffer from the dilemma of u…
Learning Physical Dynamics for Object-centric Visual Prediction
Huilin Xu, Tao Chen, Feng Xu
The ability to model the underlying dynamics of visual scenes and reason about the future is central to human intelligence. Many attempts have been made to empower intelligent syst…
Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning
Sijin Chen, Hongyuan Zhu, Mingsheng Li +6
3D dense captioning requires a model to translate its understanding of an input 3D scene into several captions associated with different object regions. Existing methods adopt a so…
Holistic Prototype Attention Network for Few-Shot VOS
Yin Tang, Tao Chen, Xiruo Jiang +3
Few-shot video object segmentation (FSVOS) aims to segment dynamic objects of unseen classes by resorting to a small set of support images that contain pixel-level object annotatio…
Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation
Tao Chen, Yazhou Yao, Jinhui Tang
Weakly supervised semantic segmentation (WSSS) models relying on class activation maps (CAMs) have achieved desirable performance comparing to the non-CAMs-based counterparts. Howe…