How Universal Are SAM2 Features?

Masoud Khairi Atani; Alon Harell; Hyomin Choi; Runyu Yang; Fabien Racape; Ivan V. Bajic

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arXiv · 2510.17051

How Universal Are SAM2 Features?

Masoud Khairi Atani·Alon Harell·Hyomin Choi·Runyu Yang·Fabien Racape·Ivan V. Bajic

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Abstract

The trade-off between general-purpose foundation vision models and their specialized counterparts is critical for efficient feature coding design and is not yet fully understood. We investigate this trade-off by comparing the feature versatility of the general-purpose Hiera encoder against the segmentation-specialized Segment Anything Model 2 (SAM2). Using a lightweight, trainable neck to probe the adaptability of their frozen features, we quantify the information-theoretic cost of specialization. Our results reveal that while SAM2's specialization is highly effective for spatially-related tasks like depth estimation, it comes at a cost. The specialized SAM2 encoder underperforms its generalist predecessor, Hiera, on conceptually distant tasks such as pose estimation and image captioning, demonstrating a measurable loss of broader semantic information. A novel cross-neck analysis on SAM2 reveals that each level of adaptation creates a further representational bottleneck. Our analysis illuminates these trade-offs in feature universality, providing a quantitative foundation for designing efficient feature coding and adaptation strategies for diverse downstream applications.

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Masoud Khairi Atani, Alon Harell, Hyomin Choi, Runyu Yang, Fabien Racape, Ivan V. Bajic. 2025-10-19. How Universal Are SAM2 Features?. https://arxiv.org/abs/2510.17051

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