Evaluating SAM2 for Video Semantic Segmentation

• URL: http://arxiv.org/abs/2512.01774v1
• Date: Mon, 01 Dec 2025 15:15:16 GMT
• Title: Evaluating SAM2 for Video Semantic Segmentation
• Authors: Syed Hesham Syed Ariff, Yun Liu, Guolei Sun, Jing Yang, Henghui Ding, Xue Geng, Xudong Jiang,
• Abstract summary: The Anything Model 2 (SAM2) has proven to be a powerful foundation model for promptable visual object segmentation in both images and videos.<n>This paper explores the extension of SAM2 to dense Video Semantic (VSS)<n>Our experiments suggest that leveraging SAM2 enhances overall performance in VSS, primarily due to its precise predictions of object boundaries.
• Score: 60.157605818225186
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The Segmentation Anything Model 2 (SAM2) has proven to be a powerful foundation model for promptable visual object segmentation in both images and videos, capable of storing object-aware memories and transferring them temporally through memory blocks. While SAM2 excels in video object segmentation by providing dense segmentation masks based on prompts, extending it to dense Video Semantic Segmentation (VSS) poses challenges due to the need for spatial accuracy, temporal consistency, and the ability to track multiple objects with complex boundaries and varying scales. This paper explores the extension of SAM2 for VSS, focusing on two primary approaches and highlighting firsthand observations and common challenges faced during this process. The first approach involves using SAM2 to extract unique objects as masks from a given image, with a segmentation network employed in parallel to generate and refine initial predictions. The second approach utilizes the predicted masks to extract unique feature vectors, which are then fed into a simple network for classification. The resulting classifications and masks are subsequently combined to produce the final segmentation. Our experiments suggest that leveraging SAM2 enhances overall performance in VSS, primarily due to its precise predictions of object boundaries.

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