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

• URL: http://arxiv.org/abs/2306.10750v1
• Date: Mon, 19 Jun 2023 07:49:29 GMT
• Title: WiCo: Win-win Cooperation of Bottom-up and Top-down Referring Image Segmentation
• Authors: Zesen Cheng, Peng Jin, Hao Li, Kehan Li, Siheng Li, Xiangyang Ji, Chang Liu and Jie Chen
• Abstract summary: We build Win-win Cooperation (WiCo) to exploit complementary nature of two types of methods on both interaction and integration aspects. With our WiCo, several prominent top-down and bottom-up combinations achieve remarkable improvements on three common datasets with reasonable extra costs.
• Score: 37.53063869243558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The top-down and bottom-up methods are two mainstreams of referring segmentation, while both methods have their own intrinsic weaknesses. Top-down methods are chiefly disturbed by Polar Negative (PN) errors owing to the lack of fine-grained cross-modal alignment. Bottom-up methods are mainly perturbed by Inferior Positive (IP) errors due to the lack of prior object information. Nevertheless, we discover that two types of methods are highly complementary for restraining respective weaknesses but the direct average combination leads to harmful interference. In this context, we build Win-win Cooperation (WiCo) to exploit complementary nature of two types of methods on both interaction and integration aspects for achieving a win-win improvement. For the interaction aspect, Complementary Feature Interaction (CFI) provides fine-grained information to top-down branch and introduces prior object information to bottom-up branch for complementary feature enhancement. For the integration aspect, Gaussian Scoring Integration (GSI) models the gaussian performance distributions of two branches and weightedly integrates results by sampling confident scores from the distributions. With our WiCo, several prominent top-down and bottom-up combinations achieve remarkable improvements on three common datasets with reasonable extra costs, which justifies effectiveness and generality of our method.

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