$\textit{Don't Guess, Just Ask}$: Resolving Ambiguity in Referring Segmentation via Multi-turn Clarification

TL;DR

This paper introduces IC-Seg, a framework that resolves ambiguous referring segmentation queries through multi-turn clarification, improving accuracy and reducing redundant interactions.

Contribution

The paper proposes a novel agentic framework with hierarchical optimization for proactive clarification in referring segmentation tasks.

Findings

IC-Seg significantly outperforms existing methods on ambiguous query resolution.

The hierarchical optimization strategy enhances dialogue efficiency and segmentation accuracy.

IC-Seg maintains state-of-the-art performance on standard benchmarks.

Abstract

Referring segmentation aims to segment the target objects in images or videos based on the textual query. Despite remarkable progress over the past years, existing works always assume that the user-provided queries are already precise and clear. However, this assumption is impractical. In real-world scenarios, it is unrealistic to expect all users to thoroughly review their visual content and carefully ensure their queries are unique and unambiguous. When encountering such cases, existing segmentation models tend to arbitrarily guess the user preferences, often resulting in undesired outcomes. To address this limitation, we propose \textbf{IC-Seg}, a novel agentic framework that proactively clarifies user intent through multi-turn conversation before segmentation. To effectively incentivize this capability, we further introduce \textbf{Hi-GRPO}, a new hierarchical optimization strategy that injects dense and informative supervision signals at the trajectory, turn, and step levels. This strategy encourages efficient intent clarification, effectively eliminating redundant interactions and improving overall dialogue quality. For evaluation, we establish \textbf{Ambi-RVOS}, a referring video object segmentation benchmark with ambiguous user queries. Extensive experiments demonstrate that IC-Seg not only outperforms existing methods by a large margin in resolving ambiguous queries, but also maintains state-of-the-art performance on standard reasoning segmentation benchmarks. Code and data will be released at \url{https://github.com/iSEE-Laboratory/IC-Seg}.