EfficientSAM3: Progressive Hierarchical Distillation for Video Concept Segmentation from SAM1, 2, and 3

TL;DR

EfficientSAM3 introduces a progressive hierarchical distillation approach to create lightweight, on-device capable models for video concept segmentation that closely mimic the performance of the larger SAM3 model.

Contribution

The paper proposes a novel three-stage distillation framework, PHD, to efficiently transfer capabilities from SAM3 to smaller models suitable for on-device use.

Findings

Achieves strong performance-efficiency trade-offs on VOS datasets.

Enables on-device concept segmentation and tracking.

Maintains high fidelity to the teacher model.

Abstract

The Segment Anything Model 3 (SAM3) advances visual understanding with Promptable Concept Segmentation (PCS) across images and videos, but its unified architecture (shared vision backbone, DETR-style detector, dense-memory tracker) remains prohibitive for on-device use. We present EfficientSAM3, a family of efficient models built on Progressive Hierarchical Distillation (PHD) that transfers capability from SAM3 to lightweight students in three stages: (1) Encoder Distillation aligns image features via prompt-in-the-loop training on SA-1B; (2) Temporal Memory Distillation replaces dense memory with a compact Perceiver-based module trained on SA-V to compress and retrieve spatiotemporal features efficiently; and (3) End-to-End Fine-Tuning refines the full pipeline on the official SAM3 PCS data to preserve concept-level performance. PHD yields a spectrum of student variants using RepViT, TinyViT, and EfficientViT backbones, enabling on-device concept segmentation and tracking while maintaining high fidelity to teacher behavior. We benchmark on popular VOS datasets, and compare with varies of releated work, achieing strong performance-efficiency trade-offs.