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20192026
most citedVisual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problem

4 citations · 6 across the 7 of their papers we have counts for

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cs.CV2026

Beyond Entropy: Self-Diagnostic Multi-Role Token Optimization for Video Reasoning

Yudong Han, Yong Wang, Zaiquan Yang +4

Reinforcement learning with verifiable rewards has substantially advanced multimodal reasoning, yet it remains fundamentally limited by ambiguous token-level credit assignment. Whi…

cs.CV2026

Visual Enhanced Depth Scaling for Multimodal Latent Reasoning

Yudong Han, Yong Wang, Zaiquan Yang +3

Multimodal latent reasoning has emerged as a promising paradigm that replaces explicit Chain-of-Thought (CoT) decoding with implicit feature propagation, simultaneously enhancing r…

cs.CV2026

Mema: Memory-Augmented Adapter for Enhanced Vision-Language Understanding

Ying Liu, Yudong Han, Kean Shi +1

Multimodal Large Language Models (MLLMs) have achieved remarkable performance by aligning pretrained visual representations with the linguistic knowledge embedded in Large Language…

cs.CV2024

DynFocus: Dynamic Cooperative Network Empowers LLMs with Video Understanding

Yudong Han, Qingpei Guo, Liyuan Pan +3

The challenge in LLM-based video understanding lies in preserving visual and semantic information in long videos while maintaining a memory-affordable token count. However, redunda…

cs.CV2022★ 4 cited

Visual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problem

Yudong Han, Liqiang Nie, Jianhua Yin +2

Several studies have recently pointed that existing Visual Question Answering (VQA) models heavily suffer from the language prior problem, which refers to capturing superficial sta…

cs.CV2022★ 2 cited

Semantic-aware Modular Capsule Routing for Visual Question Answering

Yudong Han, Jianhua Yin, Jianlong Wu +2

Visual Question Answering (VQA) is fundamentally compositional in nature, and many questions are simply answered by decomposing them into modular sub-problems. The recent proposed…