LEARNING WITH MEMORY FOR FEW-SHOT SEMANTIC SEGMENTATION

Despite great progress made in the few-shot semantic segmentation task, the existing works still suffer from problems of incompleteness and inconsistency of segmentation. In this paper, a novel attention-aided LSTM optimization network called LONet is proposed, which optimizes predictions without forgetting useful inner cues. Particularly, we calculate an attention map to align and match possible locations with query features to deal with incomplete segmentation. Then, an LSTM-based module is designed to overcome the segmentation inconsistency by memorizing and updating useful cues iteratively. Extensive experiments are conducted on two popular few-shot segmentation datasets including PASCAL-5(i) and FSS-1000. The experimental results on the FSS-1000 dataset demonstrate that our LONet exceeds the state-of-the-art results by 2.1% and 2.3%, respectively.