Region Mutual Information Loss for Semantic Segmentation

TL;DR

This paper introduces a region mutual information (RMI) loss for semantic segmentation that models pixel dependencies efficiently, improving accuracy without extra testing overhead by maximizing the mutual information between predicted and true high-dimensional pixel relationships.

Contribution

The paper proposes a novel RMI loss that captures pixel dependencies using mutual information, offering an efficient alternative to existing methods requiring additional model complexity.

Findings

RMI improves segmentation performance on PASCAL VOC 2012.

RMI achieves consistent gains on CamVid dataset.

No additional inference cost during testing.

Abstract

Semantic segmentation is a fundamental problem in computer vision. It is considered as a pixel-wise classification problem in practice, and most segmentation models use a pixel-wise loss as their optimization riterion. However, the pixel-wise loss ignores the dependencies between pixels in an image. Several ways to exploit the relationship between pixels have been investigated, \eg, conditional random fields (CRF) and pixel affinity based methods. Nevertheless, these methods usually require additional model branches, large extra memories, or more inference time. In this paper, we develop a region mutual information (RMI) loss to model the dependencies among pixels more simply and efficiently. In contrast to the pixel-wise loss which treats the pixels as independent samples, RMI uses one pixel and its neighbour pixels to represent this pixel. Then for each pixel in an image, we get a multi-dimensional point that encodes the relationship between pixels, and the image is cast into a multi-dimensional distribution of these high-dimensional points. The prediction and ground truth thus can achieve high order consistency through maximizing the mutual information (MI) between their multi-dimensional distributions. Moreover, as the actual value of the MI is hard to calculate, we derive a lower bound of the MI and maximize the lower bound to maximize the real value of the MI. RMI only requires a few extra computational resources in the training stage, and there is no overhead during testing. Experimental results demonstrate that RMI can achieve substantial and consistent improvements in performance on PASCAL VOC 2012 and CamVid datasets. The code is available at https://github.com/ZJULearning/RMI.