Due to the homogeneous backgrounds and cluttered layouts of remote sensing (RS) imagery, fine-grained local entities are indispensable for preventing cross-modal semantic mismatch. However, existing parameter-efficient fine-tuning (PEFT) methods based on contrastive language-image pretraining (CLIP) encapsulate scenes...
Meng-Ru Ma, Jia-Xu Guo, Wen-Ping Ma et al.· IEEE Transactions on Geoscie...· 0 citations
BRL is proposed, a novel adapter-based PEFT framework that facilitates hierarchical, bidirectional information flow within both token-mixing and channel-mixing layers of frozen foundation models, achieving state-of-the-art performance while requiring less than 0.5% backbone parameter updates.
Xiaoqiang Lu, Li-Cheng Jiao, Ling-Ling Li et al.· 0 citations
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 tas...
Xiaoqiang Lu, Li-Cheng Jiao, Long Sun et al.· arXiv.org· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.