Research map / Detection and segmentation / Open-set and few-shot detection
Detecting Camouflaged Object in Frequency Domain
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add offline DCT frequency input (ablation) | Baseline COD network · CHAMELEON, CAMO-Test, COD10K-Test | F w β: 0.809 -> 0.812; 0.743 -> 0.744; 0.680 -> 0.685 | Helped |
| Add HOR to the baseline (ablation) | Baseline COD network · CHAMELEON, CAMO-Test, COD10K-Test | F w β: 0.809 -> 0.815; 0.743 -> 0.750; 0.680 -> 0.690 | Helped |
| Add online frequency enhancement and frequency loss (ablation) | Baseline with offline frequency input · CHAMELEON, CAMO-Test, COD10K-Test | F w β: 0.812 -> 0.826; 0.744 -> 0.756; 0.685 -> 0.721 | Helped |
| Add feature alignment (ablation) | FEM+frequency-loss network · CHAMELEON, CAMO-Test, COD10K-Test | F w β: 0.826 -> 0.829; 0.756 -> 0.768; 0.721 -> 0.727 | Helped |
| Use HOR instead of self-attention (ablation) | FEM+frequency-loss+feature-alignment network · CHAMELEON, CAMO-Test, COD10K-Test | F w β: 0.833 -> 0.837; 0.774 -> 0.778; 0.729 -> 0.731 | Helped |
Closest papers by meaning
- FD2-Net: Frequency-Driven Feature Decomposition Network for Infrared-Visible Object Detection (AAAI 2025 · similarity 0.86)
- CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models (AAAI 2024 · similarity 0.85)
- Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects (ICLR 2024 · similarity 0.84)
- Shift the Lens: Environment-Aware Unsupervised Camouflaged Object Detection (CVPR 2025 · similarity 0.84)
- UCOD-DPL: Unsupervised Camouflaged Object Detection via Dynamic Pseudo-label Learning (CVPR 2025 · similarity 0.83)
- SdalsNet: Self-Distilled Attention Localization and Shift Network for Unsupervised Camouflaged Object Detection (AAAI 2025 · similarity 0.83)
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