7 citations · 11 across the 6 of their papers we have counts for
5 papers · 1 filter
OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference
Xiangyu Zhao, Shengyuan Ding, Zicheng Zhang +10
Recent advancements in open-source multi-modal large language models (MLLMs) have primarily focused on enhancing foundational capabilities, leaving a significant gap in human prefe…
MG-LLaVA: Towards Multi-Granularity Visual Instruction Tuning
Xiangyu Zhao, Xiangtai Li, Haodong Duan +4
Multi-modal large language models (MLLMs) have made significant strides in various visual understanding tasks. However, the majority of these models are constrained to process low-…
Auto Cherry-Picker: Learning from High-quality Generative Data Driven by Language
Yicheng Chen, Xiangtai Li, Yining Li +4
Diffusion models can generate realistic and diverse images, potentially facilitating data availability for data-intensive perception tasks. However, leveraging these models to boos…
MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding
Xinyu Fang, Kangrui Mao, Haodong Duan +4
The advent of large vision-language models (LVLMs) has spurred research into their applications in multi-modal contexts, particularly in video understanding. Traditional VideoQA be…
An Open and Comprehensive Pipeline for Unified Object Grounding and Detection
Xiangyu Zhao, Yicheng Chen, Shilin Xu +4
Grounding-DINO is a state-of-the-art open-set detection model that tackles multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Exp…