# IRFNet: Cognitive-Inspired Iterative Refinement Fusion Network for Camouflaged Object Detection

## TL;DR

This paper introduces IRFNet, a new network for detecting camouflaged objects by mimicking human visual cognition and improving feature enhancement and iterative optimization.

## Contribution

The novel IRFNet framework uses hierarchical feature enhancement and context-guided iterative optimization for better camouflaged object detection.

## Key findings

- IRFNet outperforms 14 state-of-the-art methods on three benchmark datasets with improvements of 0.9–13.7% in key metrics.
- Ablation studies confirm the effectiveness of the hierarchical feature enhancement and iterative optimization components.
- The framework preserves fine details while modeling multiscale patterns effectively.

## Abstract

Camouflaged Object Detection (COD) aims to identify objects that are intentionally concealed within their surroundings through appearance, texture, or pattern adaptations. Despite recent advances, extreme object–background similarity causes existing methods struggle with accurately capturing discriminative features and effectively modeling multiscale patterns while preserving fine details. To address these challenges, we propose Iterative Refinement Fusion Network (IRFNet), a novel framework that mimics human visual cognition through progressive feature enhancement and iterative optimization. Our approach incorporates the following: (1) a Hierarchical Feature Enhancement Module (HFEM) coupled with a dynamic channel-spatial attention mechanism, which enriches multiscale feature representations through bilateral and trilateral fusion pathways; and (2) a Context-guided Iterative Optimization Framework (CIOF) that combines transformer-based global context modeling with iterative refinement through dual-branch supervision. Extensive experiments on three challenging benchmark datasets (CAMO, COD10K, and NC4K) demonstrate that IRFNet consistently outperforms fourteen state-of-the-art methods, achieving improvements of 0.9–13.7% across key metrics. Comprehensive ablation studies validate the effectiveness of each proposed component and demonstrate how our iterative refinement strategy enables progressive improvement in detection accuracy.