2 papers
cs.CV2026
Universal Skeleton Understanding via Differentiable Rendering and MLLMs
Ziyi Wang, Peiming Li, Xinshun Wang +3
Multimodal large language models (MLLMs) exhibit strong visual-language reasoning, yet cannot process structured, non-visual data such as human skeletons. Existing methods either c…
cs.LG2025
Set a Thief to Catch a Thief: Combating Label Noise through Noisy Meta Learning
Hanxuan Wang, Na Lu, Xueying Zhao +4
Learning from noisy labels (LNL) aims to train high-performance deep models using noisy datasets. Meta learning based label correction methods have demonstrated remarkable performa…