Weakly-supervised instance co-segmentation via tensor-based salient co-peak search

Instance co-segmentation aims to segment the co-occurrent instances among two images. This task heavily relies on instance-related cues provided by co-peaks, which are generally estimated by exhaustively exploiting all paired candidates in point-to-point patterns. However, such patterns could yield a high number of false-positive co-peaks, resulting in over-segmentation whenever there are mutual occlusions. To tackle with this issue, this paper proposes an instance co-segmentation method via tensor-based salient co-peak search (TSCPS-ICS). The proposed method explores high-order correlations via triple-to-triple matching among feature maps to find reliable co-peaks with the help of co-saliency detection. The proposed method is shown to capture more accurate intra-peaks and inter-peaks among feature maps, reducing the false-positive rate of co-peak search. Upon having accurate co-peaks, one can efficiently infer responses of the targeted instance. Experiments on four benchmark datasets validate the superior performance of the proposed method.