1 citations · 1 across the 24 of their papers we have counts for
8 papers · 2 filters
SpatialDreamer: Incentivizing Spatial Reasoning via Active Mental Imagery
Meng Cao, Xingyu Li, Xue Liu +2
Despite advancements in Multi-modal Large Language Models (MLLMs) for scene understanding, their performance on complex spatial reasoning tasks requiring mental simulation remains…
Seeing through Imagination: Learning Scene Geometry via Implicit Spatial World Modeling
Meng Cao, Haokun Lin, Haoyuan Li +6
Spatial reasoning, the ability to understand and interpret the 3D structure of the world, is a critical yet underdeveloped capability in Multimodal Large Language Models (MLLMs). C…
Thinking with Drafts: Speculative Temporal Reasoning for Efficient Long Video Understanding
Pengfei Hu, Meng Cao, Yingyao Wang +6
Long video understanding is essential for human-like intelligence, enabling coherent perception and reasoning over extended temporal contexts. While the emerging thinking-with-fram…
Video Spatial Reasoning with Object-Centric 3D Rollout
Haoran Tang, Meng Cao, Ruyang Liu +4
Recent advances in Multi-modal Large Language Models (MLLMs) have showcased remarkable capabilities in vision-language understanding. However, enabling robust video spatial reasoni…
COLT: Enhancing Video Large Language Models with Continual Tool Usage
Yuyang Liu, Meng Cao, Xinyuan Shi +1
The success of Large Language Models (LLMs) has significantly propelled the research of video understanding. To harvest the benefits of well-trained expert models (i.e., tools), vi…
X-SAM: From Segment Anything to Any Segmentation
Hao Wang, Limeng Qiao, Zequn Jie +6
Large Language Models (LLMs) demonstrate strong capabilities in broad knowledge representation, yet they are inherently deficient in pixel-level perceptual understanding. Although…
Ground-R1: Incentivizing Grounded Visual Reasoning via Reinforcement Learning
Meng Cao, Haoze Zhao, Can Zhang +3
Large Vision-Language Models (LVLMs) have become powerful general-purpose assistants, yet their predictions often lack reliability and interpretability due to insufficient groundin…
Video SimpleQA: Towards Factuality Evaluation in Large Video Language Models
Meng Cao, Pengfei Hu, Yingyao Wang +12
Recent advancements in Large Video Language Models (LVLMs) have highlighted their potential for multi-modal understanding, yet evaluating their factual grounding in videos remains…