4 citations · 9 across the 6 of their papers we have counts for
6 papers · 1 filter
Dispider: Enabling Video LLMs with Active Real-Time Interaction via Disentangled Perception, Decision, and Reaction
Rui Qian, Shuangrui Ding, Xiaoyi Dong +5
Active Real-time interaction with video LLMs introduces a new paradigm for human-computer interaction, where the model not only understands user intent but also responds while cont…
OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?
Yifei Li, Junbo Niu, Ziyang Miao +12
Temporal Awareness, the ability to reason dynamically based on the timestamp when a question is raised, is the key distinction between offline and online video LLMs. Unlike offline…
SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree
Shuangrui Ding, Rui Qian, Xiaoyi Dong +6
The Segment Anything Model 2 (SAM 2) has emerged as a powerful foundation model for object segmentation in both images and videos, paving the way for various downstream video appli…
Rethinking Image-to-Video Adaptation: An Object-centric Perspective
Rui Qian, Shuangrui Ding, Dahua Lin
Image-to-video adaptation seeks to efficiently adapt image models for use in the video domain. Instead of finetuning the entire image backbone, many image-to-video adaptation parad…
Streaming Long Video Understanding with Large Language Models
Rui Qian, Xiaoyi Dong, Pan Zhang +4
This paper presents VideoStreaming, an advanced vision-language large model (VLLM) for video understanding, that capably understands arbitrary-length video with a constant number o…
Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation
Shuangrui Ding, Rui Qian, Haohang Xu +2
In this paper, we propose a simple yet effective approach for self-supervised video object segmentation (VOS). Our key insight is that the inherent structural dependencies present…