Evaluating SAM2 for Video Semantic Segmentation

2025-12-01

AI Summary

This work addresses three key challenges in video semantic segmentation (VSS): low spatial precision, poor temporal consistency, and difficulty modeling complex multi-object boundaries and scale variations. To this end, we introduce the Segmentation Anything Model 2 (SAM2) into dense VSS for the first time, proposing two novel fusion paradigms: (1) a parallel architecture that leverages SAM2 to generate high-fidelity initial masks, jointly optimized with a semantic segmentation network; and (2) a feature-driven architecture that extracts temporally aligned region features using SAM2 masks, followed by lightweight semantic classification and multi-frame result fusion. We further propose three innovations: mask generation with explicit temporal awareness, cross-frame feature alignment, and adaptive mask fusion. Experiments demonstrate significant improvements in boundary accuracy and inter-frame consistency across multiple VSS benchmarks—particularly for fine-grained object segmentation and long-term tracking scenarios.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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.

Problem

• Extending SAM2 for dense Video Semantic Segmentation
• Addressing spatial accuracy and temporal consistency challenges
• Tracking multiple objects with complex boundaries and varying scales

Innovation

• Extracting unique object masks using SAM2
• Classifying features with a simple network
• Combining classifications and masks for segmentation