DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding
Multi-task visual grounding requires models to jointly understand linguistic semantics and perform accurate visual localization and segmentation. Despite the success of multimodal large language models, effectively adapting them to multiple grounding objectives remains challenging. Existing methods commonly enforce task cooperation through shared representations, while overlooking the intrinsic conflict between task-oriented feature interests. In this paper, we introduce $\textbf{DeCo}$, an efficient $\textbf{De}$couple-to-$\textbf{Co}$uple learning framework that resolves this dilemma through a two-stage paradigm: task-specific representation decoupling followed by complementary prior co...
| Authors | Xiaoqiang Lu, Licheng Jiao, Long Sun et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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