1 citations · 1 across the 7 of their papers we have counts for
6 papers · 1 filter
ReCap: Retrieval-Guided Refinement for Image Captioning Enhancement via Reinforcement Learning
Haonan Jia, Shichao Dong, Zenghui Sun +7
Reinforcement Learning (RL) has demonstrated significant gains in image captioning, yet it is still limited in encouraging Large Vision-Language Models (LVLMs) to explore novel rea…
Cross-modal Identity Mapping: Minimizing Information Loss in Modality Conversion via Reinforcement Learning
Haonan Jia, Shichao Dong, Xin Dong +6
Large Vision-Language Models (LVLMs) often omit or misrepresent critical visual content in generated image captions. Minimizing such information loss will force LVLMs to focus on i…
INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling
Xin Dong, Shichao Dong, Jin Wang +7
Hallucinations in large vision-language models (LVLMs) pose significant challenges for real-world applications, as LVLMs may generate responses that appear plausible yet remain inc…
Forensics-Bench: A Comprehensive Forgery Detection Benchmark Suite for Large Vision Language Models
Jin Wang, Chenghui Lv, Xian Li +6
Recently, the rapid development of AIGC has significantly boosted the diversities of fake media spread in the Internet, posing unprecedented threats to social security, politics, l…
Towards RGB-NIR Cross-modality Image Registration and Beyond
Huadong Li, Shichao Dong, Jin Wang +5
This paper focuses on the area of RGB(visible)-NIR(near-infrared) cross-modality image registration, which is crucial for many downstream vision tasks to fully leverage the complem…
Diagnosing the Compositional Knowledge of Vision Language Models from a Game-Theoretic View
Jin Wang, Shichao Dong, Yapeng Zhu +4
Compositional reasoning capabilities are usually considered as fundamental skills to characterize human perception. Recent studies show that current Vision Language Models (VLMs) s…