3 citations · 6 across the 16 of their papers we have counts for
9 papers · 1 filter
Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models
Yolo Y. Tang, Jing Bi, Pinxin Liu +24
Video understanding represents the most challenging frontier in computer vision, requiring models to reason about complex spatiotemporal relationships, long-term dependencies, and…
Diagnosing Visual Reasoning: Challenges, Insights, and a Path Forward
Jing Bi, Guangyu Sun, Ali Vosoughi +2
Multimodal large language models (MLLMs) that integrate visual and textual reasoning leverage chain-of-thought (CoT) prompting to tackle complex visual tasks, yet continue to exhib…
MMPerspective: Do MLLMs Understand Perspective? A Comprehensive Benchmark for Perspective Perception, Reasoning, and Robustness
Yolo Y. Tang, Pinxin Liu, Zhangyun Tan +11
Understanding perspective is fundamental to human visual perception, yet the extent to which multimodal large language models (MLLMs) internalize perspective geometry remains uncle…
Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting
Yunlong Tang, Jing Bi, Chao Huang +16
We present CAT-V (Caption AnyThing in Video), a training-free framework for fine-grained object-centric video captioning that enables detailed descriptions of user-selected objects…
VERIFY: A Benchmark of Visual Explanation and Reasoning for Investigating Multimodal Reasoning Fidelity
Jing Bi, Junjia Guo, Susan Liang +8
Visual reasoning is central to human cognition, enabling individuals to interpret and abstractly understand their environment. Although recent Multimodal Large Language Models (MLL…
EAGLE: Egocentric AGgregated Language-video Engine
Jing Bi, Yunlong Tang, Luchuan Song +3
The rapid evolution of egocentric video analysis brings new insights into understanding human activities and intentions from a first-person perspective. Despite this progress, the…