EFTNet: an efficient fine-tuning method for few-shot segmentation

Few-shot segmentation (FSS) aims to segment novel classes given a small number of labeled samples. Most of the existing studies do not fine-tune the model during meta-testing, thus biasing the model towards the base classes and preventing the prediction of novel classes. Other studies only use support images for fine-tuning, which biases the model towards the support images rather than the target query images, especially when there is a large difference between the support and the query images. To alleviate these issues, we propose an e ̲ fficient f ̲ ine- t ̲ uning network (EFTNet) that uses unlabeled query images and predicted pseudo labels to fine-tune the trained model parameters during meta-testing, which can bias the model towards the target query images. In addition, we design a query-to-support module, a support-to-query module, and a discrimination module to evaluate which fine-tuning round the model achieves optimal results. Moreover, the query-to-support module also takes the query images and their pseudo masks as part of the support images and support masks, which causes the prototypes to contain query information and tend to obtain better predictions. As a new meta-testing scheme, our EFTNet can be easily combined with existing studies and greatly improve their model performance without repeating the meta-training phase. Many experiments on PASCAL- 5 i and COCO- 20 i prove the effectiveness of our EFTNet. The EFTNet also achieves new state-of-the-art performance. Codes are available at https://github.com/Jiaguang-NEU/EFTNet .