Context-aware Cross-level Fusion Network for Camouflaged Object Detection (2105.12555v1)

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.

• The paper introduces C2F-Net, a novel architecture that fuses multi-level features to enhance the precision of camouflaged object detection.
• It employs an Attention-induced Cross-level Fusion Module and a Dual-branch Global Context Module to integrate multi-scale attention and global semantics.
• Quantitative results on CHAMELEON, CAMO, and COD10K datasets demonstrate superior performance over 14 state-of-the-art models, especially in complex occlusion scenarios.

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

Camouflaged Object Detection (COD) is a significant challenge in the field of computer vision due to the inherently low boundary contrast between the object and its surrounding environment. This paper introduces a novel approach to tackle this issue by proposing the Context-aware Cross-level Fusion Network (C$^2$F-Net), designed to improve the accuracy of detecting camouflaged objects in diverse scenarios.

The C$^2$F-Net architecture incorporates two key modules: the Attention-induced Cross-level Fusion Module (ACFM) and the Dual-branch Global Context Module (DGCM), both operating on the concept of leveraging multi-scale feature representations effectively. The ACFM is fundamental in integrating multi-level features with informative attention coefficients, creating a fusion mechanism that refines features based on a Multi-Scale Channel Attention (MSCA) component. This attention mechanism is tailored to address variations in shape and size, contributing towards scalable object detection capabilities.

Meanwhile, the DGCM is adept at exploiting rich context information by transforming input features through parallel branches to gather and integrate dense global semantics. The architecture is noteworthy for using a cascaded approach on high-level features, efficiently synergizing multi-level information while emphasizing context awareness.

Quantitative experiments demonstrate the superiority of C$^2$F-Net over existing strategies. Across three benchmark datasets—CHAMELEON, CAMO, and COD10K—C$^2$F-Net consistently achieves higher scores in metrics like $S_{\alpha}$, $E_{\phi}$, $F_{\beta}^w$, and $M$, conclusively outperforming 14 state-of-the-art models in COD accuracy. Particularly, the improvement is substantial in scenarios that include multiple objects or occlusion, where traditional methods often struggle.

The implications of this research are twofold. Practically, C$^2$F-Net could revolutionize applications requiring precise detection of camouflaged objects, such as surveillance and wildlife monitoring. Theoretically, the framework opens avenues for further exploration into hierarchical feature fusion and multi-scale context aggregation, which could be adapted for other complex detection tasks in AI.

Future directions might explore extending C$^2$F-Net into dynamic contexts or improving computational efficiency for real-time application. Additionally, examining how this network architecture could be integrated with other deep learning paradigms may offer further advancements in intelligent perception systems. Through its robust performance and novel approach to feature integration and context exploitation, C$^2$F-Net represents a meaningful contribution to the advancement of camouflaged object detection methodologies.