Video Object Segmentation via SAM 2: The 4th Solution for LSVOS Challenge VOS Track

• URL: http://arxiv.org/abs/2408.10125v2
• Date: Sat, 24 Aug 2024 13:07:51 GMT
• Title: Video Object Segmentation via SAM 2: The 4th Solution for LSVOS Challenge VOS Track
• Authors: Feiyu Pan, Hao Fang, Runmin Cong, Wei Zhang, Xiankai Lu,
• Abstract summary: Segment Anything Model 2 (SAM 2) is a foundation model towards solving promptable visual segmentation in images and videos. SAM 2 builds a data engine, which improves model and data via user interaction, to collect the largest video segmentation dataset to date. Without fine-tuning on the training set, SAM 2 achieved 75.79 J&F on the test set and ranked 4th place for 6th LSVOS Challenge VOS Track.
• Score: 28.52754012142431
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Object Segmentation (VOS) task aims to segmenting a particular object instance throughout the entire video sequence given only the object mask of the first frame. Recently, Segment Anything Model 2 (SAM 2) is proposed, which is a foundation model towards solving promptable visual segmentation in images and videos. SAM 2 builds a data engine, which improves model and data via user interaction, to collect the largest video segmentation dataset to date. SAM 2 is a simple transformer architecture with streaming memory for real-time video processing, which trained on the date provides strong performance across a wide range of tasks. In this work, we evaluate the zero-shot performance of SAM 2 on the more challenging VOS datasets MOSE and LVOS. Without fine-tuning on the training set, SAM 2 achieved 75.79 J&F on the test set and ranked 4th place for 6th LSVOS Challenge VOS Track.

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