7 papers · 1 filter
Cross-Modal Attention Calibration for LVLM Hallucination Mitigation
Jiaming Li, Jiacheng Zhang, Zequn Jie +2
Large vision-language models (LVLMs) have shown remarkable capabilities in visual-language understanding. Despite their success, LVLMs still suffer from generating hallucinations i…
UniToken: Harmonizing Multimodal Understanding and Generation through Unified Visual Encoding
Yang Jiao, Haibo Qiu, Zequn Jie +4
We introduce UniToken, an auto-regressive generation model that encodes visual inputs through a combination of discrete and continuous representations, enabling seamless integratio…
TimeMarker: A Versatile Video-LLM for Long and Short Video Understanding with Superior Temporal Localization Ability
Shimin Chen, Xiaohan Lan, Yitian Yuan +2
Rapid development of large language models (LLMs) has significantly advanced multimodal large language models (LMMs), particularly in vision-language tasks. However, existing video…
MindBench: A Comprehensive Benchmark for Mind Map Structure Recognition and Analysis
Lei Chen, Feng Yan, Yujie Zhong +3
Multimodal Large Language Models (MLLM) have made significant progress in the field of document analysis. Despite this, existing benchmarks typically focus only on extracting text…
Fewer Tokens and Fewer Videos: Extending Video Understanding Abilities in Large Vision-Language Models
Shimin Chen, Yitian Yuan, Shaoxiang Chen +2
Amidst the advancements in image-based Large Vision-Language Models (image-LVLM), the transition to video-based models (video-LVLM) is hindered by the limited availability of quali…
AlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning
Duojun Huang, Xinyu Xiong, Jie Ma +4
Powered by massive curated training data, Segment Anything Model (SAM) has demonstrated its impressive generalization capabilities in open-world scenarios with the guidance of prom…