TinySAM 2: Extreme Memory Compression for Efficient Track Anything Model

TL;DR

TinySAM 2 is a lightweight, resource-efficient video segmentation model that maintains high performance by employing memory quality management and token compression techniques, enabling practical deployment.

Contribution

The paper introduces TinySAM 2, a novel lightweight model with a memory management mechanism and token compression, reducing computational costs while retaining 90% of SAM 2's performance.

Findings

Achieves 90% of SAM 2.1's performance on DAVIS and SA-V datasets.

Uses only 7% memory tokens and 3% training data compared to SAM 2.

Significantly reduces parameter count and computational load for practical deployment.

Abstract

Segment Anything Model 2 (SAM 2) serves as a core foundation model in the field of video segmentation. Building upon the original SAM model, it introduces a memory bank mechanism and demonstrates outstanding performance in tasks such as semi-supervised video object segmentation and tracking anything. However, the complex computational characteristics of SAM 2's multi-stage image encoder and memory module have raised the barrier to the model's deployment in practical applications. To address this issue, we propose TinySAM 2, a lightweight video segmentation model that balances performance and efficiency. First, a memory quality management mechanism is introduced to select and retain high-informative historical frames as the memory. In addition, a joint-spatial-temporal token compression is proposed that reduces the memory storage and computational cost. Specifically, average pooling is employed to first compress redundancy tokens in the spatial domain. In the temporal domain, informative tokens are selected across frames in the memory bank based on token-level similarity measurement. Besides, we take RepViT as the lightweight image encoder, which further reduces the model parameters. Extensive experiments on challenging datasets such as DAVIS and SA-V demonstrate that TinySAM 2 achieves 90% of the performance of SAM 2.1, with only 7% memory tokens and 3% training data. This study effectively alleviates the bottlenecks in parameter count, computational load, and deployment costs associated with SAM 2, providing a resource-efficient solution for the widespread application of video segmentation models on devices.