10 citations · 20 across the 16 of their papers we have counts for
9 papers · 1 filter
MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding
Fuwen Luo, Shengfeng Lou, Chi Chen +9
Video temporal understanding is crucial for multimodal large language models (MLLMs) to reason over events in videos. Despite recent advances in general video understanding, curren…
KARL: Knowledge-Aware Reasoning and Reinforcement Learning for Knowledge-Intensive Visual Grounding
Xinyu Ma, Ziyang Ding, Zhicong Luo +6
Knowledge-Intensive Visual Grounding (KVG) requires models to localize objects using fine-grained, domain-specific entity names rather than generic referring expressions. Although…
EscapeCraft: A 3D Room Escape Environment for Benchmarking Complex Multimodal Reasoning Ability
Ziyue Wang, Yurui Dong, Fuwen Luo +5
The rapid advancing of Multimodal Large Language Models (MLLMs) has spurred interest in complex multimodal reasoning tasks in the real-world and virtual environment, which require…
StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding
Junming Lin, Zheng Fang, Chi Chen +5
The rapid development of Multimodal Large Language Models (MLLMs) has expanded their capabilities from image comprehension to video understanding. However, most of these MLLMs focu…
ActiView: Evaluating Active Perception Ability for Multimodal Large Language Models
Ziyue Wang, Chi Chen, Fuwen Luo +6
Active perception, a crucial human capability, involves setting a goal based on the current understanding of the environment and performing actions to achieve that goal. Despite si…
CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models
Fuwen Luo, Chi Chen, Zihao Wan +11
Multimodal large language models (MLLMs) have demonstrated promising results in a variety of tasks that combine vision and language. As these models become more integral to researc…
Model Composition for Multimodal Large Language Models
Chi Chen, Yiyang Du, Zheng Fang +8
Recent developments in Multimodal Large Language Models (MLLMs) have shown rapid progress, moving towards the goal of creating versatile MLLMs that understand inputs from various m…
Position-Enhanced Visual Instruction Tuning for Multimodal Large Language Models
Chi Chen, Ruoyu Qin, Fuwen Luo +4
Recently, Multimodal Large Language Models (MLLMs) that enable Large Language Models (LLMs) to interpret images through visual instruction tuning have achieved significant success.…
Weakly Supervised Vision-and-Language Pre-training with Relative Representations
Chi Chen, Peng Li, Maosong Sun +1
Weakly supervised vision-and-language pre-training (WVLP), which learns cross-modal representations with limited cross-modal supervision, has been shown to effectively reduce the d…