Adaptive Intra-Group Aggregation for Co-Saliency Detection

Co-salient object detection (CoSOD) together with the rapid development of deep learning has led to substantial progress in recent years. However, the feature aggregation between group feature representation and individual feature representation is still a challenging issue. In this work, we propose a novel adaptive intra-group aggregation (AIGA) method, which provides a new perspective to investigate the interaction relationship between group and single-image features and aggregate these features in an adaptive way. A novel scale-aware loss is proposed to help the model capture the scale prior of different groups and discriminatively process groups during the training phase. Extensive experiments demonstrate that the proposed method can effectively improve the performance without increasing extra parameters and achieve better accuracy on three prevalent benchmarks.