Exploring Figure-Ground Assignment Mechanism in Perceptual Organization

Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track

Bibtex Paper Supplemental


Wei Zhai, Yang Cao, Jing Zhang, Zheng-Jun Zha


Perceptual organization is a challenging visual task that aims to perceive and group the individual visual element so that it is easy to understand the meaning of the scene as a whole. Most recent methods building upon advanced Convolutional Neural Network (CNN) come from learning discriminative representation and modeling context hierarchically. However, when the visual appearance difference between foreground and background is obscure, the performance of existing methods degrades significantly due to the visual ambiguity in the discrimination process. In this paper, we argue that the figure-ground assignment mechanism, which conforms to human vision cognitive theory, can be explored to empower CNN to achieve a robust perceptual organization despite visual ambiguity. Specifically, we present a novel Figure-Ground-Aided (FGA) module to learn the configural statistics of the visual scene and leverage it for the reduction of visual ambiguity. Particularly, we demonstrate the benefit of using stronger supervisory signals by teaching (FGA) module to perceive configural cues, \ie, convexity and lower region, that human deem important for the perceptual organization. Furthermore, an Interactive Enhancement Module (IEM) is devised to leverage such configural priors to assist representation learning, thereby achieving robust perception organization with complex visual ambiguities. In addition, a well-founded visual segregation test is designed to validate the capability of the proposed FGA mechanism explicitly. Comprehensive evaluation results demonstrate our proposed FGA mechanism can effectively enhance the capability of perception organization on various baseline models. Nevertheless, the model augmented via our proposed FGA mechanism also outperforms state-of-the-art approaches on four challenging real-world applications.