3 papers
cs.CV2026
Shot-Aware Frame Sampling for Video Understanding
Mengyu Zhao, Di Fu, Yongyu Xie +4
Video frame sampling is essential for efficient long-video understanding with Vision-Language Models (VLMs), since dense inputs are costly and often exceed context limits. Yet when…
cs.CV2026
GAP-MLLM: Geometry-Aligned Pre-training for Activating 3D Spatial Perception in Multimodal Large Language Models
Jiaxin Zhang, Junjun Jiang, Haijie Li +3
Multimodal Large Language Models (MLLMs) demonstrate exceptional semantic reasoning but struggle with 3D spatial perception when restricted to pure RGB inputs. Despite leveraging i…
cs.CV2025
Mitigating Task Objective Conflicts for Continual Learning in Unified Multimodal Models
Jiaxing Zhang, Hao Tang
Recent advances in unified autoregressive (AR) models have enabled multimodal understanding (MMU) and text-to-image generation (T2I) within a single architecture. However, in conti…