FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection

TL;DR

This paper introduces FocusDiffuser, a novel generative diffusion model with specialized modules, that significantly improves camouflaged object detection by capturing subtle details, outperforming existing methods on multiple benchmarks.

Contribution

The paper presents a new diffusion-based approach with Boundary-Driven LookUp and Cyclic Positioning modules to enhance camouflaged object detection capabilities.

Findings

Outperforms existing models on CAMO, COD10K, NC4K benchmarks.

Effectively captures subtle details in complex environments.

Demonstrates the potential of generative models for camouflaged object detection.

Abstract

Detecting objects seamlessly blended into their surroundings represents a complex task for both human cognitive capabilities and advanced artificial intelligence algorithms. Currently, the majority of methodologies for detecting camouflaged objects mainly focus on utilizing discriminative models with various unique designs. However, it has been observed that generative models, such as Stable Diffusion, possess stronger capabilities for understanding various objects in complex environments; Yet their potential for the cognition and detection of camouflaged objects has not been extensively explored. In this study, we present a novel denoising diffusion model, namely FocusDiffuser, to investigate how generative models can enhance the detection and interpretation of camouflaged objects. We believe that the secret to spotting camouflaged objects lies in catching the subtle nuances in details. Consequently, our FocusDiffuser innovatively integrates specialized enhancements, notably the Boundary-Driven LookUp (BDLU) module and Cyclic Positioning (CP) module, to elevate standard diffusion models, significantly boosting the detail-oriented analytical capabilities. Our experiments demonstrate that FocusDiffuser, from a generative perspective, effectively addresses the challenge of camouflaged object detection, surpassing leading models on benchmarks like CAMO, COD10K and NC4K.