Multi-scale Interactive Network for Salient Object Detection

TL;DR

This paper introduces a multi-scale interactive network with aggregate and self-interaction modules, along with a consistency-enhanced loss, to improve salient object detection across variable scales and categories, outperforming existing methods.

Contribution

The paper proposes a novel multi-scale interactive network with aggregate and self-interaction modules, and a consistency-enhanced loss, for improved salient object detection.

Findings

Outperforms 23 state-of-the-art methods on five benchmark datasets.

Effectively handles scale variation and class imbalance issues.

No post-processing needed for competitive results.

Abstract

Deep-learning based salient object detection methods achieve great progress. However, the variable scale and unknown category of salient objects are great challenges all the time. These are closely related to the utilization of multi-level and multi-scale features. In this paper, we propose the aggregate interaction modules to integrate the features from adjacent levels, in which less noise is introduced because of only using small up-/down-sampling rates. To obtain more efficient multi-scale features from the integrated features, the self-interaction modules are embedded in each decoder unit. Besides, the class imbalance issue caused by the scale variation weakens the effect of the binary cross entropy loss and results in the spatial inconsistency of the predictions. Therefore, we exploit the consistency-enhanced loss to highlight the fore-/back-ground difference and preserve the intra-class consistency. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches. The source code will be publicly available at https://github.com/lartpang/MINet.