How Universal Are SAM2 Features?
This work addresses the problem of feature universality for vision model designers, providing quantitative insights into the trade-offs between specialization and generalization, though it is incremental in nature.
The paper investigates the trade-off between general-purpose and specialized vision models by comparing the Hiera encoder and SAM2, finding that SAM2's specialization excels in spatially-related tasks like depth estimation but underperforms on conceptually distant tasks such as pose estimation and image captioning, with a measurable loss of broader semantic information.
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.