SAM3-I: Segment Anything with Instructions

TL;DR

SAM3-I extends the Segment Anything Model to interpret complex natural-language instructions for segmentation, integrating concept grounding and instruction reasoning within a unified framework.

Contribution

It introduces SAM3-I, a unified segmentation framework that directly interprets natural-language instructions, and HMPL-Instruct, a large-scale instruction-centric dataset for training.

Findings

SAM3-I achieves strong performance in instruction-following segmentation tasks.

The model effectively combines concept grounding with complex instruction understanding.

Code and dataset are publicly available at the provided GitHub link.

Abstract

Segment Anything Model 3 (SAM3) advances open-vocabulary segmentation through promptable concept segmentation, enabling users to segment all instances associated with a given concept using short noun-phrase (NP) prompts. While effective for concept-level grounding, real-world interactions often involve far richer natural-language instructions that combine attributes, relations, actions, states, or implicit reasoning. Currently, SAM3 relies on external multi-modal agents to convert complex instructions into NPs and conducts iterative mask filtering, leading to coarse representations and limited instance specificity. In this work, we present SAM3-I, an instruction-following extension of the SAM family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework. Built upon SAM3, SAM3-I introduces an instruction-aware cascaded adaptation mechanism with dedicated alignment losses that progressively aligns expressive instruction semantics with SAM3's vision-language representations, enabling direct interpretation of natural-language instructions while preserving its strong concept recall ability. To enable instruction-following learning, we introduce HMPL-Instruct, a large-scale instruction-centric dataset that systematically covers hierarchical instruction semantics and diverse target granularities. Experiments demonstrate that SAM3-I achieves appealing performance across referring and reasoning-based segmentation, showing that SAM3 can be effectively extended to follow complex natural-language instructions without sacrificing its original concept-driven strengths. Code and dataset are available at https://github.com/debby-0527/SAM3-I.