Papers › One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos

One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos

29 Sep 2024arXiv:2409.19603archive 2025-07-28

Zechen Bai, Tong He, Haiyang Mei, Pichao Wang, Ziteng Gao, Joya Chen, Lei Liu, Zheng Zhang, 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.

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sigmoid_ce_loss showlab/videolisa/model/VideoLISA.py official repository ran · fixture could not drive it fingerprinted Apache-2.0 (permissive) · a9292f5d89194794 · report
uniform_sample showlab/videolisa/chat.py official repository ran Apache-2.0 (permissive) · 1b9b4f3219b0ece5 · report
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parse_args showlab/videolisa/merge_lora_weights_and_save_hf_model.py official repository unverified Apache-2.0 (permissive) · b89c913c2723201f · report
parse_args showlab/videolisa/chat.py official repository unverified Apache-2.0 (permissive) · 83529642e413f1c1 · report
parse_args showlab/videolisa/train_joint.py official repository unverified Apache-2.0 (permissive) · 50fab0bafe08b1a9 · report
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Tasks

AllImage SegmentationLanguage ModelingLanguage ModellingLarge Language ModelMultimodal Large Language ModelObjectObject TrackingReasoning SegmentationReferring Video Object SegmentationSegmentationSemantic SegmentationVideo Object SegmentationVideo Semantic SegmentationWorld Knowledge

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Referring Video Object Segmentation Long-RVOS VideoLISA J&F 33.1 #7 of 7 Archive leaderboard report
Referring Video Object Segmentation Long-RVOS VideoLISA tIoU 69.6 #7 of 7 Archive leaderboard report
Referring Video Object Segmentation Long-RVOS VideoLISA vIoU 28.2 #7 of 7 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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