Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning

Abstract

Weakly-supervised semantic segmentation is a challenging task as no pixel-wise label information is provided for training. Recent methods have exploited classification networks to localize objects by selecting regions with strong response. While such response map provides sparse information, however, there exist strong pairwise relations between pixels in natural images, which can be utilized to propagate the sparse map to a much denser one. In this paper, we propose an iterative algorithm to learn such pairwise relations, which consists of two branches, a unary segmentation network which learns the label probabilities for each pixel, and a pairwise affinity network which learns affinity matrix and refines the probability map generated from the unary network. The refined results by the pairwise network are then used as supervision to train the unary network, and the procedures are conducted iteratively to obtain better segmentation progressively. To learn reliable pixel affinity without accurate annotation, we also propose to mine confident regions. We show that iteratively training this framework is equivalent to optimizing an energy function with convergence to a local minimum. Experimental results on the PASCAL VOC 2012 and COCO datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.

IJCV 2020

References in corpus (2)

• STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation
• Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

Cited by in corpus (7)

• Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation
• Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation
• Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation
• Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation
• 360 Layout Estimation via Orthogonal Planes Disentanglement and Multi-view Geometric Consistency Perception
• Cognition Transferring and Decoupling for Text-supervised Egocentric Semantic Segmentation
• Learning with Weak Annotations for Robust Maritime Obstacle Detection