You Do Not Need Additional Priors in Camouflage Object Detection

TL;DR

This paper introduces a new method for camouflage object detection that does not require additional prior information, instead leveraging multi-layer feature aggregation to guide the detection process effectively.

Contribution

The paper proposes an adaptive feature aggregation technique that eliminates the need for extra priors, improving detection performance using only image feature information.

Findings

Achieves comparable or better performance than state-of-the-art methods.

Effectively combines multi-layer features for guidance.

Does not rely on edge or ranking priors.

Abstract

Camouflage object detection (COD) poses a significant challenge due to the high resemblance between camouflaged objects and their surroundings. Although current deep learning methods have made significant progress in detecting camouflaged objects, many of them heavily rely on additional prior information. However, acquiring such additional prior information is both expensive and impractical in real-world scenarios. Therefore, there is a need to develop a network for camouflage object detection that does not depend on additional priors. In this paper, we propose a novel adaptive feature aggregation method that effectively combines multi-layer feature information to generate guidance information. In contrast to previous approaches that rely on edge or ranking priors, our method directly leverages information extracted from image features to guide model training. Through extensive experimental results, we demonstrate that our proposed method achieves comparable or superior performance when compared to state-of-the-art approaches.