SimToken: A Simple Baseline for Referring Audio-Visual Segmentation

• URL: http://arxiv.org/abs/2509.17537v2
• Date: Tue, 23 Sep 2025 04:04:41 GMT
• Title: SimToken: A Simple Baseline for Referring Audio-Visual Segmentation
• Authors: Dian Jin, Yanghao Zhou, Jinxing Zhou, Jiaqi Ma, Ruohao Guo, Dan Guo,
• Abstract summary: Referring Audio-Visual (Ref-AVS) aims to segment specific objects in videos based on natural language expressions.<n>This task poses significant challenges in cross-modal reasoning and fine-grained object localization.<n>We propose a framework, SimToken, that integrates a multimodal large language model (MLLM) with the Segment Anything Model (SAM)
• Score: 29.88252418748085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring Audio-Visual Segmentation (Ref-AVS) aims to segment specific objects in videos based on natural language expressions involving audio, vision, and text information. This task poses significant challenges in cross-modal reasoning and fine-grained object localization. In this paper, we propose a simple framework, SimToken, that integrates a multimodal large language model (MLLM) with the Segment Anything Model (SAM). The MLLM is guided to generate a special semantic token representing the referred object. This compact token, enriched with contextual information from all modalities, acts as a prompt to guide SAM to segment objectsacross video frames. To further improve semantic learning, we introduce a novel target-consistent semantic alignment loss that aligns token embeddings from different expressions but referring to the same object. Experiments on the Ref-AVS benchmark demonstrate that our approach achieves superior performance compared to existing methods.

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