4 papers · 1 filter
BVB: Benchmarking Agentic Video Understanding via Programmatic Reconstruction in Blender
Yolo Y. Tang, Daiki Shimada, Jiayue Meng +14
Multimodal agents can create complex videos in software such as Blender by writing code instead of using diffusion models. Yet video understanding benchmarks still evaluate models…
Suppression Is Not Forgetting: Residual Recoverability in Visual Concept Unlearning for VLMs
Zhangyun Tan, Zeliang Zhang, Jiani Liu +5
Vision-language models (VLMs) may need to forget visual concepts after deployment because of privacy, copyright, licensing, safety, or policy changes. Conventional machine unlearni…
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…
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…