How Universal Are SAM2 Features?

2025-10-19

AI Summary

This work investigates the trade-off between feature expressiveness and information-theoretic cost when specializing general-purpose vision models (e.g., Hiera) for segmentation tasks via architectures like SAM2. We propose a cross-layer neck adaptation analysis framework: the backbone is frozen, while lightweight trainable neck modules are inserted per layer; information entropy and cross-layer representation similarity are jointly leveraged to quantify semantic information loss induced by specialization. Experiments show that SAM2 excels in spatially local tasks (e.g., segmentation) but underperforms significantly on long-range semantic tasks (e.g., pose estimation, image captioning) compared to Hiera. Moreover, layer-wise neck adaptation exacerbates representational bottlenecks, confirming that specialization incurs a systematic penalty on feature generality. To our knowledge, this is the first study to systematically characterize—through an information-theoretic lens—the inherent constraints specialization imposes on feature multifunctionality, offering a novel paradigm for designing efficient visual encoders.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Compares generalist Hiera versus specialized SAM2 feature versatility
• Quantifies information cost of specialization across vision tasks
• Analyzes representational bottlenecks in adaptation strategies

Innovation

• Compares SAM2 and Hiera encoders via lightweight trainable neck
• Quantifies specialization cost using information-theoretic measures
• Reveals adaptation bottlenecks through cross-neck analysis