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How Universal Are SAM2 Features?

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

TL;DR

The paper addresses how universal SAM2 features compare to generalist Hiera under adaptation with a trainable neck, and whether specialization incurs information loss. It introduces a neck-based adaptation framework and a cross-neck analysis to quantify information bottlenecks via multiple MI estimators and distributional metrics. Results show SAM2 excels on depth (spatial tasks) but loses versatility on pose and captioning, while Hiera maintains broader semantic utility; distillation and higher neck capacity mitigate but do not fully erase these trade-offs. The findings provide a quantitative foundation for designing efficient universal feature coding and adaptation strategies.

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.

How Universal Are SAM2 Features?

TL;DR

The paper addresses how universal SAM2 features compare to generalist Hiera under adaptation with a trainable neck, and whether specialization incurs information loss. It introduces a neck-based adaptation framework and a cross-neck analysis to quantify information bottlenecks via multiple MI estimators and distributional metrics. Results show SAM2 excels on depth (spatial tasks) but loses versatility on pose and captioning, while Hiera maintains broader semantic utility; distillation and higher neck capacity mitigate but do not fully erase these trade-offs. The findings provide a quantitative foundation for designing efficient universal feature coding and adaptation strategies.

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.
Paper Structure (12 sections, 2 equations, 5 figures, 3 tables)

This paper contains 12 sections, 2 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Mutual Information Estimation
  4. Experimental Setup
  5. Downstream Task Configurations
  6. Information-Theoretic Metrics
  7. Results
  8. Overall Task Performance
  9. Impact of Knowledge Distillation and Neck Capacity
  10. Feature-Level Information Analysis
  11. Cross-Task Adaptation and Representational Bottlenecks
  12. Conclusion

Figures (5)

  • Figure 1: Conceptual diagram of the information captured by an image encoder (inner ellipse) relative to total image information (outer ellipse). We quantify the encoder's latent knowledge on diverse vision tasks (colored ellipses).
  • Figure 2: A unified model architecture serving as a template for each vision task in single-neck adaptation setting. Our framework uses a frozen encoder, a frozen task decoder (head), and a trainable neck.
  • Figure 3: Cross-neck model designed to measure the representational bottleneck of sequential task adaptation by using features from a frozen, pre-trained neck (task 1 neck) to train a second neck (task 2 neck) on a new task.
  • Figure 4: The effect of Neck size on performance in distillation setting. Performance is measured in RMSE for Depth (lower is better), AP for Pose (higher is better), and CIDEr for Captioning (higher is better).
  • Figure 5: Normalized feature distance/similarity, and mutual information (MI) vs. neck complexity for the SAM2 encoder across Depth Estimation (top), Pose Estimation (middle), and Image Captioning (bottom). For visualization purposes, each feature distance/similarity metric is independently scaled to its observed data range with 10% padding at both ends to improve visual clarity.