Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection

TL;DR

This paper introduces a weakly-supervised RGB-D salient object detection model that leverages mutual information regularization for effective multimodal representation learning and prediction refinement, achieving competitive results with fully supervised methods.

Contribution

It proposes a novel mutual information regularization framework for disentangled multimodal representation learning and a multimodal variational auto-encoder for prediction refinement in weakly-supervised RGB-D salient object detection.

Findings

Achieves comparable performance to fully supervised models on benchmark datasets.

Demonstrates effectiveness of mutual information regularization in multimodal learning.

Validates the proposed stochastic refinement improves detection accuracy.

Abstract

In this paper, we present a weakly-supervised RGB-D salient object detection model via scribble supervision. Specifically, as a multimodal learning task, we focus on effective multimodal representation learning via inter-modal mutual information regularization. In particular, following the principle of disentangled representation learning, we introduce a mutual information upper bound with a mutual information minimization regularizer to encourage the disentangled representation of each modality for salient object detection. Based on our multimodal representation learning framework, we introduce an asymmetric feature extractor for our multimodal data, which is proven more effective than the conventional symmetric backbone setting. We also introduce multimodal variational auto-encoder as stochastic prediction refinement techniques, which takes pseudo labels from the first training stage as supervision and generates refined prediction. Experimental results on benchmark RGB-D salient object detection datasets verify both effectiveness of our explicit multimodal disentangled representation learning method and the stochastic prediction refinement strategy, achieving comparable performance with the state-of-the-art fully supervised models. Our code and data are available at: https://github.com/baneitixiaomai/MIRV.