6 papers · 1 filter
Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation
Xingyu Zhu, Junfeng Fang, Shuo Wang +4
Large Vision-Language Models (LVLMs) exhibit powerful generative capabilities but frequently produce hallucinations that compromise output reliability. Fine-tuning on annotated dat…
Look Carefully: Adaptive Visual Reinforcements in Multimodal Large Language Models for Hallucination Mitigation
Xingyu Zhu, Kesen Zhao, Liang Yi +4
Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language reasoning, yet they remain vulnerable to hallucination, where generated content deviat…
Res-Bench: Benchmarking the Robustness of Multimodal Large Language Models to Dynamic Resolution Input
Chenxu Li, Zhicai Wang, Yuan Sheng +3
Multimodal Large Language Models (MLLMs) increasingly support dynamic image resolutions. However, current evaluation paradigms primarily assess semantic performance, overlooking th…
UniSVG: A Unified Dataset for Vector Graphic Understanding and Generation with Multimodal Large Language Models
Jinke Li, Jiarui Yu, Chenxing Wei +5
Unlike bitmap images, scalable vector graphics (SVG) maintain quality when scaled, frequently employed in computer vision and artistic design in the representation of SVG code. In…
Dynamic Multimodal Prototype Learning in Vision-Language Models
Xingyu Zhu, Shuo Wang, Beier Zhu +6
With the increasing attention to pre-trained vision-language models (VLMs), \eg, CLIP, substantial efforts have been devoted to many downstream tasks, especially in test-time adapt…
BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs
Zhantao Yang, Ruili Feng, Keyu Yan +13
Advancements in large Vision-Language Models have brought precise, accurate image captioning, vital for advancing multi-modal image understanding and processing. Yet these captions…