Context-aware Cross-level Fusion Network for Camouflaged Object Detection

TL;DR

This paper introduces C2F-Net, a novel deep learning model that effectively detects camouflaged objects by integrating multi-level features with attention mechanisms and global context modules, significantly outperforming existing methods.

Contribution

The paper presents a new network architecture with attention-induced cross-level fusion and dual-branch global context modules for improved camouflaged object detection.

Findings

C2F-Net outperforms state-of-the-art models on benchmark datasets.

The proposed modules effectively integrate multi-level features and global context.

Extensive experiments validate the model's superior performance.

Abstract

Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings. In addition, the appearance of camouflaged objects varies significantly, e.g., object size and shape, aggravating the difficulties of accurate COD. In this paper, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net) to address the challenging COD task. Specifically, we propose an Attention-induced Cross-level Fusion Module (ACFM) to integrate the multi-level features with informative attention coefficients. The fused features are then fed to the proposed Dual-branch Global Context Module (DGCM), which yields multi-scale feature representations for exploiting rich global context information. In C2F-Net, the two modules are conducted on high-level features using a cascaded manner. Extensive experiments on three widely used benchmark datasets demonstrate that our C2F-Net is an effective COD model and outperforms state-of-the-art models remarkably. Our code is publicly available at: https://github.com/thograce/C2FNet.