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 ...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.展开更多
基金supported in part by the National Natural Science Foundation of China (Grant Nos.U21A20520,62172112)the Key-Area Research and Development of Guangdong Province (2022A0505050014,2020B1111190001)+1 种基金the National Key Research and Development Program of China (2022YFE0112200)the Key-Area Research and Development Program of Guangzhou City (202206030009).
文摘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.
