Position Is All You Need: A Free Lunch Token Compression Strategy for MLLM-based Referring Expression Segmentation

The paper introduces PAYN, a training‑free token compression strategy for Multimodal Large Language Model (MLLM) based Referring Expression Segmentation (RES). By preserving original position embeddings and local spatial structures, PAYN retains tokens that are evenly distributed across neighboring regions, thereby maintaining spatial relational consistency. Experiments on multiple RES benchmarks show that PAYN outperforms existing token compression methods, confirming that position information alone is sufficient for effective compression in this task.

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• llms
• rag
• computer-vision
• multimodal
• benchmarks