6 papers · 1 filter
Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering
Qiming Li, Xiaocheng Feng, Yixuan Ma +4
Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) demonstrate strong reasoning capabilities, yet their performance in English significantly outperforms that in…
CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering
Qiming Li, Zekai Ye, Xiaocheng Feng +9
Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviate from visual information, l…
Open-Source Image Editing Models Are Zero-Shot Vision Learners
Wei Liu, Jiaxin Lin, Rui Chen
Recent studies have shown that large generative models can solve vision tasks they were not explicitly trained for. However, existing evidence relies on closed-source models~(Veo~3…
DinoDental: Benchmarking DINOv3 as a Unified Vision Encoder for Dental Image Analysis
Kun Tang, Xinquan Yang, Mianjie Zheng +6
The scarcity and high cost of expert annotations in dental imaging present a significant challenge for the development of AI in dentistry. DINOv3, a state-of-the-art, self-supervis…
CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models
Qiming Li, Zekai Ye, Xiaocheng Feng +8
Although Large Vision-Language Models (LVLMs) have demonstrated powerful capabilities in interpreting visual information, they frequently produce content that deviates from visual…
Cross-Lingual Text-Rich Visual Comprehension: An Information Theory Perspective
Xinmiao Yu, Xiaocheng Feng, Yun Li +10
Recent Large Vision-Language Models (LVLMs) have shown promising reasoning capabilities on text-rich images from charts, tables, and documents. However, the abundant text within su…