Regression Residual Reasoning with Pseudo-labeled Contrastive Learning for Uncovering Multiple Complex Compositional Relations

Visual Reasoning (AVR) has been widely studied in literature. Our study reveals that AVR models tend to rely on appearance matching rather than a genuine understanding of underlying rules. We hence develop a challenging benchmark, Multiple Complex Compositional Reasoning (MC2R), composed of diverse compositional rules on attributes with intentionally increased variations. It aims to identify two outliers from five given images, in contrast to single-answer questions in previous AVR tasks. To solve MC2R tasks, a Regression Residual Reasoning with Pseudo-labeled Contrastive Learning (R3PCL) is proposed, which first transforms the original problem by selecting three images following the same rule, and iteratively regresses one normal image by using the other two, allowing the model to gradually comprehend the underlying rules. The proposed PCL leverages a set of min-max operations to generate more reliable pseudo labels, and exploits contrastive learning with data augmentation on pseudo-labeled images to boost the discrimination and generalization of features. Experimental results on two AVR datasets show that the proposed R3PCL significantly outperforms state-of-the-art models.