# Frequency Perception Network for Camouflaged Object Detection

arXiv: https://arxiv.org/abs/2308.08924 · HTML: https://mlsanity.com/paper/2308.08924/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 76 references that are in the map.
- First posted on arXiv 17 Aug 2023.

## Builds on (most cited first, 30 of 40)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [Anabranch Network for Camouflaged Object Segmentation](https://mlsanity.com/paper/2105.09451/) (2021)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)
- [Concealed Object Detection](https://mlsanity.com/paper/2102.10274/) (2021)
- [Simultaneously Localize, Segment and Rank the Camouflaged Objects](https://mlsanity.com/paper/2103.04011/) (2021)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [F3Net: Fusion, Feedback and Focus for Salient Object Detection](https://mlsanity.com/paper/1911.11445/) (2019)
- [Camouflaged Object Segmentation with Distraction Mining](https://mlsanity.com/paper/2104.10475/) (2021)
- [Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection](https://mlsanity.com/paper/2203.02688/) (2022)
- [Receptive Field Block Net for Accurate and Fast Object Detection](https://mlsanity.com/paper/1711.07767/) (2017)
- [Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution](https://mlsanity.com/paper/1904.05049/) (2019)
- [Global Context-Aware Progressive Aggregation Network for Salient Object Detection](https://mlsanity.com/paper/2003.00651/) (2020)
- [Context-aware Cross-level Fusion Network for Camouflaged Object Detection](https://mlsanity.com/paper/2105.12555/) (2021)
- [Boundary-Guided Camouflaged Object Detection](https://mlsanity.com/paper/2207.00794/) (2022)
- [Review of Visual Saliency Detection with Comprehensive Information](https://mlsanity.com/paper/1803.03391/) (2018)
- [RGB-D Salient Object Detection with Cross-Modality Modulation and Selection](https://mlsanity.com/paper/2007.07051/) (2020)
- [Deep Residual Learning in the JPEG Transform Domain](https://mlsanity.com/paper/1812.11690/) (2018)
- [CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection](https://mlsanity.com/paper/2210.02843/) (2022)
- [Does Thermal Really Always Matter for RGB-T Salient Object Detection?](https://mlsanity.com/paper/2210.04266/) (2022)
- [Learning with Multiclass AUC: Theory and Algorithms](https://mlsanity.com/paper/2107.13171/) (2021)
- [Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2108.01971/) (2021)
- [Camouflaged Object Detection via Context-aware Cross-level Fusion](https://mlsanity.com/paper/2207.13362/) (2022)

## Built on by (most cited first, 30 of 37)

- [Frequency-Spatial Entanglement Learning for Camouflaged Object Detection](https://mlsanity.com/paper/2409.01686/) (2024)
- [A Survey of Camouflaged Object Detection and Beyond](https://mlsanity.com/paper/2408.14562/) (2024)
- [A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection](https://mlsanity.com/paper/2402.18922/) (2024)
- [Frequency-Guided Spatial Adaptation for Camouflaged Object Detection](https://mlsanity.com/paper/2409.12421/) (2024)
- [FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection](https://mlsanity.com/paper/2407.13133/) (2024)
- [A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends](https://mlsanity.com/paper/2311.18373/) (2023)
- [Learning Camouflaged Object Detection from Noisy Pseudo Label](https://mlsanity.com/paper/2407.13157/) (2024)
- [Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection](https://mlsanity.com/paper/2403.01968/) (2024)
- [GLCONet: Learning Multi-source Perception Representation for Camouflaged Object Detection](https://mlsanity.com/paper/2409.09588/) (2024)
- [Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations](https://mlsanity.com/paper/2503.14895/) (2025)
- [Unlocking Attributes' Contribution to Successful Camouflage: A Combined Textual and VisualAnalysis Strategy](https://mlsanity.com/paper/2408.12086/) (2024)
- [PlantCamo: Plant Camouflage Detection](https://mlsanity.com/paper/2410.17598/) (2024)
- [Adaptive Guidance Learning for Camouflaged Object Detection](https://mlsanity.com/paper/2405.02824/) (2024)
- [Mamba Capsule Routing Towards Part-Whole Relational Camouflaged Object Detection](https://mlsanity.com/paper/2410.03987/) (2024)
- [Distribution-Specific Learning for Joint Salient and Camouflaged Object Detection](https://mlsanity.com/paper/2508.06063/) (2025)
- [Cross-level Attention with Overlapped Windows for Camouflaged Object Detection](https://mlsanity.com/paper/2311.16618/) (2023)
- [Salient Object Detection From Arbitrary Modalities](https://mlsanity.com/paper/2405.03352/) (2024)
- [FMNet: Frequency-Assisted Mamba-Like Linear Attention Network for Camouflaged Object Detection](https://mlsanity.com/paper/2503.11030/) (2025)
- [C3Net: Context-Contrast Network for Camouflaged Object Detection](https://mlsanity.com/paper/2511.12627/) (2025)
- [Assisted Refinement Network Based on Channel Information Interaction for Camouflaged and Salient Object Detection](https://mlsanity.com/paper/2512.11369/) (2025)
- [YUV20K: A Complexity-Driven Benchmark and Trajectory-Aware Alignment Model for Video Camouflaged Object Detection](https://mlsanity.com/paper/2604.09985/) (2026)
- [AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2412.17601/) (2024)
- [CamoSAM2: SAM2-oriented Prompt Auto-Refinement for Video Camouflaged Object Detection](https://mlsanity.com/paper/2504.00375/) (2025)
- [RealCamo: Boosting Real Camouflage Synthesis with Layout Controls and Textual-Visual Guidance](https://mlsanity.com/paper/2512.22974/) (2025)
- [FOAM: A General Frequency-Optimized Anti-Overlapping Framework for Overlapping Object Perception](https://mlsanity.com/paper/2506.13501/) (2025)
- [Mamba-based Spatio-Frequency Motion Perception for Video Camouflaged Object Detection](https://mlsanity.com/paper/2507.23601/) (2025)
- [SFGNet: Semantic and Frequency Guided Network for Camouflaged Object Detection](https://mlsanity.com/paper/2509.11539/) (2025)
- [SPEGNet: Synergistic Perception-Guided Network for Camouflaged Object Detection](https://mlsanity.com/paper/2510.04472/) (2025)
- [Bi-CamoDiffusion: A Boundary-informed Diffusion Approach for Camouflaged Object Detection](https://mlsanity.com/paper/2603.13357/) (2026)
- [Modality-Agnostic Prompt Learning for Multi-Modal Camouflaged Object Detection](https://mlsanity.com/paper/2604.12380/) (2026)

## Abstract (arXiv)

Camouflaged object detection (COD) aims to accurately detect objects hidden in the surrounding environment. However, the existing COD methods mainly locate camouflaged objects in the RGB domain, their performance has not been fully exploited in many challenging scenarios. Considering that the features of the camouflaged object and the background are more discriminative in the frequency domain, we propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain. Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage. With the multi-level features extracted by the backbone, we design a flexible frequency perception module based on octave convolution for coarse positioning. Then, we design the correction fusion module to step-by-step integrate the high-level features through the prior-guided correction and cross-layer feature channel association, and finally combine them with the shallow features to achieve the detailed correction of the camouflaged objects. Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets both qualitatively and quantitatively.