# Cascaded Interaction with Eroded Deep Supervision for Salient Object Detection

Source: [Cascaded Interaction with Eroded Deep Supervision for Salient Object Detection](<https://www.sota2.com/research/paper/68fa715c1e209131cea50d87>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:04:39.575Z

Authors: Hewen Xiao, Jie Mei, Guangfu Ma, Weiren Wu

Published: 2023-11-30T16:20:54.000Z

## Abstract

Deep convolutional neural networks have been widely applied in salient object detection and have achieved remarkable results in this field. However, existing models suffer from information distortion caused by interpolation during up-sampling and down-sampling. In response to this drawback, this article starts from two directions in the network: feature and label. On the one hand, a novel cascaded interaction network with a guidance module named global-local aligned attention (GAA) is designed to reduce the negative impact of interpolation on the feature side. On the other hand, a deep supervision strategy based on edge erosion is proposed to reduce the negative guidance of label interpolation on lateral output. Extensive experiments on five popular datasets demonstrate the superiority of our method.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2311.18675>)

[PDF](<https://arxiv.org/pdf/2311.18675.pdf>)