3 papers
cs.CV2026
Counterfactual Attention Policy Distillation for Temporal Video Grounding
Shaobo Ju, Haiyang Yu, Xuecheng Wu +6
Temporal video grounding is a key capability of advanced Multimodal Large Language Models (MLLMs) for the thorough understanding of video events, which is however often limited by…
cs.CV2026
ForestPrune: High-ratio Visual Token Compression for Video Multimodal Large Language Models via Spatial-Temporal Forest Modeling
Shaobo Ju, Baiyang Song, Tao Chen +6
Due to the great saving of computation and memory overhead, token compression has become a research hot-spot for MLLMs and achieved remarkable progress in image-language tasks. How…
cs.CV2025
Towards Effective Long Video Understanding of Multimodal Large Language Models via One-shot Clip Retrieval
Tao Chen, Shaobo Ju, Qiong Wu +6
Due to excessive memory overhead, most Multimodal Large Language Models (MLLMs) can only process videos of limited frames. In this paper, we propose an effective and efficient para…