SurANet: Surrounding-Aware Network for Concealed Object Detection via Highly-Efficient Interactive Contrastive Learning Strategy

TL;DR

SurANet is a novel deep network that leverages surrounding environment information and a contrastive learning strategy to improve the detection of concealed objects in cluttered scenes, outperforming existing methods.

Contribution

The paper introduces SurANet, which incorporates surrounding information into feature extraction and loss functions, and proposes a Spatial-Compressed Correlation Transmission strategy for efficient end-to-end training.

Findings

SurANet outperforms state-of-the-art methods on multiple datasets.

The surrounding-aware contrastive loss enhances object discrimination.

The proposed transmission strategy improves training efficiency.

Abstract

Concealed object detection (COD) in cluttered scenes is significant for various image processing applications. However, due to that concealed objects are always similar to their background, it is extremely hard to distinguish them. Here, the major obstacle is the tiny feature differences between the inside and outside object boundary region, which makes it trouble for existing COD methods to achieve accurate results. In this paper, considering that the surrounding environment information can be well utilized to identify the concealed objects, and thus, we propose a novel deep Surrounding-Aware Network, namely SurANet, for COD tasks, which introduces surrounding information into feature extraction and loss function to improve the discrimination. First, we enhance the semantics of feature maps using differential fusion of surrounding features to highlight concealed objects. Next, a Surrounding-Aware Contrastive Loss is applied to identify the concealed object via learning surrounding feature maps contrastively. Then, SurANet can be trained end-to-end with high efficiency via our proposed Spatial-Compressed Correlation Transmission strategy after our investigation of feature dynamics, and extensive experiments improve that such features can be well reserved respectively. Finally, experimental results demonstrate that the proposed SurANet outperforms state-of-the-art COD methods on multiple real datasets. Our source code will be available at https://github.com/kyh433/SurANet.