Research map / Detection and segmentation / CNN features
Segment, Magnify and Reiterate: Detecting Camouflaged Objects the Hard Way
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add iterative segment-and-magnify refinement stages (ablation) | SegMaR · COD10K-Test | Sα: 0.813 -> 0.833 | Helped |
| Add iterative segment-and-magnify refinement stages (ablation) | SegMaR · CAMO-Test | Sα: 0.805 -> 0.815 | Helped |
| Add iterative segment-and-magnify refinement stages (ablation) | SegMaR · CHAMELEON-Test | Sα: 0.892 -> 0.906 | Helped |
| Add the discriminative mask combining fixation and edge information (ablation) | SegMaR segmentation network · COD10K-Test | Sα: 0.810 -> 0.813 | Helped |
| Add the discriminative mask combining fixation and edge information (ablation) | SegMaR segmentation network · CAMO-Test | Sα: 0.799 -> 0.805 | Helped |
| Add the discriminative mask combining fixation and edge information (ablation) | SegMaR segmentation network · CHAMELEON-Test | Sα: 0.887 -> 0.892 | Helped |
| Use edge annotation instead of the discriminative mask (ablation) | SegMaR segmentation network · COD10K-Test | Sα: 0.809 -> 0.813 | Hurt |
| Use fixation annotation instead of the discriminative mask (ablation) | SegMaR segmentation network · COD10K-Test | Sα: 0.806 -> 0.813 | Hurt |
| Add the distraction module (ablation) | SegMaR segmentation network · COD10K-Test | wF: 0.654 -> 0.682 | Helped |
| Apply four-stage iterative refinement to SINet (ablation) | SINet · COD10K-Test | Sα: 0.771 -> 0.805 | Helped |
| Apply SegMaR rather than single-stage detection | COD detection on small objects · COD10K-Test small subset | Sα: 0.796 -> 0.825 | Helped |
| Apply SegMaR rather than single-stage detection | COD detection on small objects · COD10K-Test small subset | wF: 0.598 -> 0.677 | Helped |
Closest papers by meaning
- SdalsNet: Self-Distilled Attention Localization and Shift Network for Unsupervised Camouflaged Object Detection (AAAI 2025 · similarity 0.88)
- Shift the Lens: Environment-Aware Unsupervised Camouflaged Object Detection (CVPR 2025 · similarity 0.88)
- Endow SAM with Keen Eyes: Temporal-spatial Prompt Learning for Video Camouflaged Object Detection (CVPR 2024 · similarity 0.86)
- Camouflage Anything: Learning to Hide using Controlled Out-painting and Representation Engineering (CVPR 2025 · similarity 0.86)
- CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models (AAAI 2024 · similarity 0.86)
- UCOD-DPL: Unsupervised Camouflaged Object Detection via Dynamic Pseudo-label Learning (CVPR 2025 · similarity 0.86)
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