Evaluating SAM2 for Video Semantic Segmentation
This work addresses the challenge of dense Video Semantic Segmentation for computer vision applications, but it appears incremental as it adapts an existing foundation model rather than introducing a fundamentally new method.
This paper tackles extending the Segmentation Anything Model 2 (SAM2) for Video Semantic Segmentation (VSS) by proposing two approaches: using SAM2 to extract object masks refined by a segmentation network, and classifying these masks with a simple network. The experiments show that leveraging SAM2 enhances VSS performance due to its precise object boundary predictions.
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.