GLCONet: Learning Multi-source Perception Representation for Camouflaged Object Detection

TL;DR

GLCONet introduces a global-local collaborative framework for camouflaged object detection, leveraging multi-source perception to improve feature representation and outperform existing methods.

Contribution

It proposes a novel global-local collaborative optimization strategy and an adjacent reverse decoder to enhance feature discrimination and integration in COD.

Findings

Outperforms twenty state-of-the-art methods on three datasets.

Effectively activates significant pixels in images.

Compatible with different backbone networks.

Abstract

Recently, biological perception has been a powerful tool for handling the camouflaged object detection (COD) task. However, most existing methods are heavily dependent on the local spatial information of diverse scales from convolutional operations to optimize initial features. A commonly neglected point in these methods is the long-range dependencies between feature pixels from different scale spaces that can help the model build a global structure of the object, inducing a more precise image representation. In this paper, we propose a novel Global-Local Collaborative Optimization Network, called GLCONet. Technically, we first design a collaborative optimization strategy from the perspective of multi-source perception to simultaneously model the local details and global long-range relationships, which can provide features with abundant discriminative information to boost the accuracy in detecting camouflaged objects. Furthermore, we introduce an adjacent reverse decoder that contains cross-layer aggregation and reverse optimization to integrate complementary information from different levels for generating high-quality representations. Extensive experiments demonstrate that the proposed GLCONet method with different backbones can effectively activate potentially significant pixels in an image, outperforming twenty state-of-the-art methods on three public COD datasets. The source code is available at: \https://github.com/CSYSI/GLCONet.