most citedDDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models

15 citations · 15 across the 6 of their papers we have counts for

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cs.CV2025

Intervene-All-Paths: Unified Mitigation of LVLM Hallucinations across Alignment Formats

Jiaye Qian, Ge Zheng, Yuchen Zhu +1

Despite their impressive performance across a wide range of tasks, Large Vision-Language Models (LVLMs) remain prone to hallucination. In this study, we propose a comprehensive int…

cs.CV2025

Why LVLMs Are More Prone to Hallucinations in Longer Responses: The Role of Context

Ge Zheng, Jiaye Qian, Jiajin Tang +1

Large Vision-Language Models (LVLMs) have made significant progress in recent years but are also prone to hallucination issues. They exhibit more hallucinations in longer, free-for…

cs.CV2025

Closed-Loop Transfer for Weakly-supervised Affordance Grounding

Jiajin Tang, Zhengxuan Wei, Ge Zheng +1

Humans can perform previously unexperienced interactions with novel objects simply by observing others engage with them. Weakly-supervised affordance grounding mimics this process…

cs.CV2025

MVTokenFlow: High-quality 4D Content Generation using Multiview Token Flow

Hanzhuo Huang, Yuan Liu, Ge Zheng +3

In this paper, we present MVTokenFlow for high-quality 4D content creation from monocular videos. Recent advancements in generative models such as video diffusion models and multiv…

cs.CV2023★ 15 cited

DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models

Ge Zheng, Bin Yang, Jiajin Tang +2

A long-standing goal of AI systems is to perform complex multimodal reasoning like humans. Recently, large language models (LLMs) have made remarkable strides in such multi-step re…

cs.CV2023

Temporal Collection and Distribution for Referring Video Object Segmentation

Jiajin Tang, Ge Zheng, Sibei Yang

Referring video object segmentation aims to segment a referent throughout a video sequence according to a natural language expression. It requires aligning the natural language exp…