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The top-down and bottom-up methods are two mainstreams of referringsegmentation, while both methods have their own intrinsic weaknesses. Top-downmethods are chiefly disturbed by Polar Negative (PN) errors owing to the lackof fine-grained cross-modal alignment. Bottom-up methods are mainly perturbedby Inferior Positive (IP) errors due to the lack of prior object information.Nevertheless, we discover that two types of methods are highly complementaryfor restraining respective weaknesses but the direct average combination leadsto harmful interference. In this context, we build Win-win Cooperation (WiCo)to exploit complementary nature of two types of methods on both interaction andintegration aspects for achieving a win-win improvement. For the interactionaspect, Complementary Feature Interaction (CFI) provides fine-grainedinformation to top-down branch and introduces prior object information tobottom-up branch for complementary feature enhancement. For the integrationaspect, Gaussian Scoring Integration (GSI) models the gaussian performancedistributions of two branches and weightedly integrates results by samplingconfident scores from the distributions. With our WiCo, several prominenttop-down and bottom-up combinations achieve remarkable improvements on threecommon datasets with reasonable extra costs, which justifies effectiveness andgenerality of our method.

WiCo: Win-win Cooperation of Bottom-up and Top-down Referring Image Segmentation