2 citations · 5 across the 7 of their papers we have counts for
8 papers
V-Gym: Enhancing Agentic Visual Reasoning via Skill-Data Co-Evolution
Bei Yan, Yuecong Min, Jie Zhang +3
Advances in multimodal understanding, reasoning, and tool use enable agents to tackle increasingly complex visual reasoning tasks. By distilling past execution experience into reus…
ACT Now: Preempting LVLM Hallucinations via Adaptive Context Integration
Bei Yan, Yuecong Min, Jie Zhang +2
Large Vision-Language Models (LVLMs) frequently suffer from severe hallucination issues. Existing mitigation strategies predominantly rely on isolated, single-step states to enhanc…
SHALE: A Scalable Benchmark for Fine-grained Hallucination Evaluation in LVLMs
Bei Yan, Zhiyuan Chen, Yuecong Min +4
Despite rapid advances, Large Vision-Language Models (LVLMs) still suffer from hallucinations, i.e., generating content inconsistent with input or established world knowledge, whic…
A Survey of Multimodal Hallucination Evaluation and Detection
Zhiyuan Chen, Yuecong Min, Jie Zhang +4
Multi-modal Large Language Models (MLLMs) have emerged as a powerful paradigm for integrating visual and textual information, supporting a wide range of multi-modal tasks. However,…
REVAL: A Comprehension Evaluation on Reliability and Values of Large Vision-Language Models
Jie Zhang, Zheng Yuan, Zhongqi Wang +6
The rapid evolution of Large Vision-Language Models (LVLMs) has highlighted the necessity for comprehensive evaluation frameworks that assess these models across diverse dimensions…
MM-MoralBench: A MultiModal Moral Evaluation Benchmark for Large Vision-Language Models
Bei Yan, Jie Zhang, Zhiyuan Chen +2
The rapid integration of Large Vision-Language Models (LVLMs) into critical domains necessitates comprehensive moral evaluation to ensure their alignment with human values. While e…
Dysca: A Dynamic and Scalable Benchmark for Evaluating Perception Ability of LVLMs
Jie Zhang, Zhongqi Wang, Mengqi Lei +4
Currently many benchmarks have been proposed to evaluate the perception ability of the Large Vision-Language Models (LVLMs). However, most benchmarks conduct questions by selecting…
Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Models
Bei Yan, Jie Zhang, Zheng Yuan +2
Despite the outstanding performance in multimodal tasks, Large Vision-Language Models (LVLMs) have been plagued by the issue of hallucination, i.e., generating content that is inco…