OneSEG: Improving Generalized Referring Segmentation with Semantic-Instance Decomposition

Generalized referring segmentation extends conventional referring segmentation to scenarios where a referring expression may correspond to zero, one, or multiple instances, requiring models to jointly reason about referential semantics, instance cardinality, and target existence. Existing MLLM-based methods either autoregressively generate multiple [SEG] tokens to represent individual targets, making subsequent segmentation vulnerable to missing or duplicated tokens, or employ mask queries for parallel prediction, underutilizing the MLLM's native autoregressive reasoning capability. In this work, we propose OneSEG, a decoupled framework that preserves autoregressive referential reasoning while deferring instance prediction to decoding. Specifically, OneSEG employs a single [SEG] token to capture the shared referential semantics of all potential targets. Our Semantic-to-Instance Query Decomposition (SIQD) then transforms this holistic representation into instance-aware queries conditioned on visual evidence, enabling flexible multi-instance prediction without explicit instance enumeration during multimodal reasoning. Furthermore, we introduce an Instance Evidence Verifier (IEV) that verifies decoded candidates using explicit spatial evidence. Extensive experiments on generalized referring segmentation benchmarks demonstrate that OneSEG achieves state-of-the-art performance, validating the effectiveness of decoupling semantic reasoning from instance prediction.