Research map / Detection and segmentation / Open-set and few-shot detection
Deep Camouflage Images
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace content loss with attention-aware camouflage loss (ablation) | Deep camouflage generation · Koala example (qualitative) | camouflage effect | Helped |
| Add naturalness (manifold) regularization (ablation) | Deep camouflage generation · Koala example (qualitative) | visual naturalness | Helped |
| Add total variation loss (ablation) | Deep camouflage generation · Koala example (qualitative) | boundary smoothness | Helped |
| Add attention map to camouflage loss (ablation) | Deep camouflage generation · Lion example (qualitative) | hiding effectiveness | Helped |
| Increase lambda_cam from 1e-6 to 1e-5 (ablation) | Deep camouflage generation · Horse example (qualitative) | recognition difficulty (higher = easier to spot): 1e-6 -> 1e-5 | Mixed |
| Use proposed method instead of AB, TT, PC, CAM, NS, DPS, CFPS, DPH | Camouflage generation · User study, 50 AMT raters, 1-5 scale | rating distribution | Helped |
Closest papers by meaning
- Camouflage Anything: Learning to Hide using Controlled Out-painting and Representation Engineering (CVPR 2025 · similarity 0.88)
- Shift the Lens: Environment-Aware Unsupervised Camouflaged Object Detection (CVPR 2025 · similarity 0.85)
- SdalsNet: Self-Distilled Attention Localization and Shift Network for Unsupervised Camouflaged Object Detection (AAAI 2025 · similarity 0.84)
- LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion (CVPR 2024 · similarity 0.84)
- Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects (ICLR 2024 · similarity 0.84)
- CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models (AAAI 2024 · similarity 0.84)
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