Bridging Vision And Language Encoders: Parameter-efficient Tuning For Referring Image Segmentation | Awesome LLM Papers Add your paper to Awesome LLM Papers

Bridging Vision And Language Encoders: Parameter-efficient Tuning For Referring Image Segmentation

Zunnan Xu, Zhihong Chen, Yong Zhang, Yibing Song, Xiang Wan, Guanbin Li . 2023 IEEE/CVF International Conference on Computer Vision (ICCV) 2023 – 49 citations

[Code] [Paper]   Search on Google Scholar   Search on Semantic Scholar
3d Representation Has Code ICCV

Parameter Efficient Tuning (PET) has gained attention for reducing the number of parameters while maintaining performance and providing better hardware resource savings, but few studies investigate dense prediction tasks and interaction between modalities. In this paper, we do an investigation of efficient tuning problems on referring image segmentation. We propose a novel adapter called Bridger to facilitate cross-modal information exchange and inject task-specific information into the pre-trained model. We also design a lightweight decoder for image segmentation. Our approach achieves comparable or superior performance with only 1.61% to 3.38% backbone parameter updates, evaluated on challenging benchmarks. The code is available at https://github.com/kkakkkka/ETRIS.

Similar Work