One Token to Seg Them All: Language Instructed Reasoning Segmentation in
  Videos
Authors: name: Zechen Bai, name: Tong He, name: Haiyang Mei, name: Pichao Wang, name: Ziteng Gao, name: Joya Chen, name: Lei Liu, name: Zheng Zhang, name: Mike Zheng Shou

We introduce VideoLISA, a video-based multimodal large language model
designed to tackle the problem of language-instructed reasoning segmentation in
videos. Leveraging the reasoning capabilities and world knowledge of large
language models, and augmented by the Segment Anything Model, VideoLISA
generates temporally consistent segmentation masks in videos based on language
instructions. Existing image-based methods, such as LISA, struggle with video
tasks due to the additional temporal dimension, which requires temporal dynamic
understanding and consistent segmentation across frames. VideoLISA addresses
these challenges by integrating a Sparse Dense Sampling strategy into the
video-LLM, which balances temporal context and spatial detail within
computational constraints. Additionally, we propose a One-Token-Seg-All
approach using a specially designed <TRK> token, enabling the model to segment
and track objects across multiple frames. Extensive evaluations on diverse
benchmarks, including our newly introduced ReasonVOS benchmark, demonstrate
VideoLISA's superior performance in video object segmentation tasks involving
complex reasoning, temporal understanding, and object tracking. While optimized
for videos, VideoLISA also shows promising generalization to image
segmentation, revealing its potential as a unified foundation model for
language-instructed object segmentation. Code and model will be available at:
https://github.com/showlab/VideoLISA.