Cognition-Guided Coupled Learning for Camouflaged Object Detection

Abstract

Camouflaged object detection (COD) aims to identify targets that visually blend into their surroundings, posing significant challenges due to low contrast between targets and backgrounds, diverse target appearances, and limited annotated training samples. A critical yet often overlooked subtask in COD is object presence detection, which determines whether a camouflaged object actually exists in an image. This step is essential as it underpins accurate localization and segmentation and enables timely decisions in practical applications such as disaster rescue and wildlife monitoring. However, most existing methods assume the target is always present and focus mainly on localization, limiting their robustness and reliability in complex scenarios. Inspired by the human brain's remarkable ability to perceive camouflaged objects by integrating edge and contextual cues, we propose a cognitive-visual coupling learning framework to enhance object presence detection. Specifically, electroencephalogram (EEG) signals are incorporated during training to embed human cognitive patterns into the model's visual feature learning through coupled representation learning. Notably, during inference, our framework relies solely on visual input, effectively leveraging cognitive insights acquired during training to improve detection performance. Extensive experiments demonstrate that our approach achieves superior accuracy compared to state-of-the-art methods and exhibits strong generalization across diverse visual backbone architectures and unseen cross-dataset scenarios.