LTAF-NET: LEARNING TASK-AWARE ADAPTIVE FEATURES AND REFINING MASK FOR FEW-SHOT SEMANTIC SEGMENTATION

Few shot segmentation is a newly-developing and challenging computer vision task which is only provided with few labeled samples of the novel class. Some recent works on this problem focus more on how to design an effective comparison module but ignore how to extract the features passed to compare. In this paper we propose a novel model named LTAFNet for few-shot segmentation. This model aims to adaptively recalibrate the extracted features which could boost the accuracy of dense comparison between support features and query features. Besides an additional prediction refinement module is designed to refine the initial mask. Meanwhile this method can apply to k-shot setting without developing a new specialized architecture and achieve competitive performance. Experiments on PASCAL-5(i) and FSS-1000 strongly prove the effectiveness of the proposed model. Our model outperforms the second-best method 1.4% in 1-shot and 0.76% in 5-shot respectively in PASCAL-5(i).