SANSA: Unleashing the Hidden Semantics in SAM2 for Few-Shot Segmentation (2505.21795v1)

Abstract: Few-shot segmentation aims to segment unseen object categories from just a handful of annotated examples. This requires mechanisms that can both identify semantically related objects across images and accurately produce segmentation masks. We note that Segment Anything 2 (SAM2), with its prompt-and-propagate mechanism, offers both strong segmentation capabilities and a built-in feature matching process. However, we show that its representations are entangled with task-specific cues optimized for object tracking, which impairs its use for tasks requiring higher level semantic understanding. Our key insight is that, despite its class-agnostic pretraining, SAM2 already encodes rich semantic structure in its features. We propose SANSA (Semantically AligNed Segment Anything 2), a framework that makes this latent structure explicit, and repurposes SAM2 for few-shot segmentation through minimal task-specific modifications. SANSA achieves state-of-the-art performance on few-shot segmentation benchmarks specifically designed to assess generalization, outperforms generalist methods in the popular in-context setting, supports various prompts flexible interaction via points, boxes, or scribbles, and remains significantly faster and more compact than prior approaches. Code is available at https://github.com/ClaudiaCuttano/SANSA.

Overview of SANSA: Unleashing the Hidden Semantics in SAM2 for Few-Shot Segmentation

The paper "SANSA: Unleashing the Hidden Semantics in SAM2 for Few-Shot Segmentation" investigates the potential of Segment Anything 2 (SAM2) for few-shot segmentation tasks. The research identifies the inherent semantic capabilities within SAM2 that are not fully exploited due to the task-specific cues predominantly optimized for object tracking. The authors propose a framework, SANSA, which modifies SAM2 minimally to leverage its latent semantic structure, enhancing its efficacy in few-shot segmentation without extensive additional computational overhead.

Few-shot segmentation (FSS) is a challenging task that requires segmenting novel object categories using very limited examples. Traditional models for semantic segmentation fall short when dealing with unseen categories, highlighting the need for approaches that can generalize effectively in open-world scenarios. SAM2, known for its prompt-and-propagate mechanism designed for Video Object Segmentation, offers built-in feature matching capabilities. Yet, its pretraining focused on object tracking results in representations entangled with low-level signals, limiting its direct application to tasks needing deeper semantic understanding.

The cornerstone of this paper is the hypothesis that SAM2 inherently encodes a rich semantic structure within its features that can be disentangled and repurposed for few-shot segmentation. SANSA achieves state-of-the-art performance on few-shot segmentation benchmarks engineered to gauge generalization capabilities, surpasses several established methods in in-context settings, and supports flexible annotation prompts such as points, boxes, or scribbles. Additionally, SANSA retains substantial efficiency, being faster and more compact compared to existing solutions.

Key Contributions:

1. Semantic Structure Encapsulation: The paper explores the latent semantic structure within SAM2. By employing lightweight adaptations like AdaptFormer, SANSA extracts and makes explicit the semantic content embedded in SAM2 features, enabling semantic-level tracking across images instead of mere visual similarity.
2. Performance and Efficiency: SANSA demonstrates superior performance in various benchmarks, including LVIS-92$^i$, COCO-20$^i$, and FSS-1000, in both 1-shot and 5-shot segmentation tasks. The adapted framework achieves faster inference speeds, outperforming competitors significantly while maintaining model compactness.
3. Prompt Versatility: The ability to utilize diverse prompts ensures SANSA's wide applicability in downstream tasks without necessity for pixel-level precision in reference annotations, thus facilitating practical applications like scalable data annotation.
4. Analytical Insights: Through visualizations using principal component analysis, the paper illustrates the concentration of semantic information within specific dimensions of the feature space post-adaptation, which is less apparent in frozen SAM2 features, underscoring the effectiveness of SANSA's feature transformation.

Implications and Speculations:

The findings of this paper extend beyond few-shot segmentation. By accurately exposing and utilizing the semantic structure within SAM2, SANSA represents an advancement toward more adaptable AI systems capable of handling diverse tasks with minimal supervision. This research may inspire future methodologies in adaptable AI models where latent semantic capabilities are leveraged across different domains. Furthermore, the approach emphasizes the utility of foundation models in enabling rapid development of specialized applications without extensive retraining, indicating promising pathways for efficient model deployment in resource-constrained environments.

This paper effectively demonstrates that SAM2—and potentially other visual foundational models—may house greater task adaptability than their originally intended use, pointing to a broader landscape of applications in autonomous systems where high-level semantic comprehension is paramount. Future developments in AI might increasingly focus on extracting such latent capabilities to overcome the limitations of conventional models in dynamic real-world settings.