arXiv Computer Vision September 22, 2026 By Xiaoqiang Lu, Licheng Jiao, Long Sun, Yuting Yang, Xu Liu, Lingling Li, Wenping Ma, Fang Liu

DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding

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DeCo introduces an efficient decouple-to-couple learning framework for multi-task visual grounding, addressing conflicts between localization and segmentation tasks. It first applies Task-aware Semantic Decoupling (TSD) to separate shared visual cues into task-specific features guided by salient words, then uses Hybrid Prior Coupling (HPC) to merge sentence-level semantic priors with mask-derived spatial priors for improved grounding. Experiments across multiple natural and remote sensing datasets show that DeCo achieves state‑of‑the‑art performance while requiring only lightweight trainable parameters on a frozen multimodal encoder.

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