2 citations · 2 across the 6 of their papers we have counts for
8 papers · 1 filter
TimeThink: Reasoning with Time for Video LLMs
Handong Li, Longteng Guo, Zikang Liu +8
Video reasoning requires models to identify and verify temporally localized evidence within long video sequences. Recent Video Large Language Models (Video-LLMs) have shown promisi…
AdaSpark: Adaptive Sparsity for Efficient Long-Video Understanding
Handong Li, Zikang Liu, Longteng Guo +10
Processing long-form videos with Video Large Language Models (Video-LLMs) is computationally prohibitive. Current efficiency methods often compromise fine-grained perception throug…
Thinking in Streaming Video
Zikang Liu, Longteng Guo, Handong Li +7
Real-time understanding of continuous video streams is essential for interactive assistants and multimodal agents operating in dynamic environments. However, most existing video re…
Breaking the Encoder Barrier for Seamless Video-Language Understanding
Handong Li, Yiyuan Zhang, Longteng Guo +2
Most Video-Large Language Models (Video-LLMs) adopt an encoder-decoder framework, where a vision encoder extracts frame-wise features for processing by a language model. However, t…
Explore the Limits of Omni-modal Pretraining at Scale
Yiyuan Zhang, Handong Li, Jing Liu +1
We propose to build omni-modal intelligence, which is capable of understanding any modality and learning universal representations. In specific, we propose a scalable pretraining p…
COSA: Concatenated Sample Pretrained Vision-Language Foundation Model
Sihan Chen, Xingjian He, Handong Li +3
Due to the limited scale and quality of video-text training corpus, most vision-language foundation models employ image-text datasets for pretraining and primarily focus on modelin…