Research map / Video / Video segmentation and tracking
Implicit Motion Handling for Video Camouflaged Object Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use implicit short-term correlation and long-term temporal refinement | SLT-Net · MoCA-Mask | Sα: 0.614 -> 0.631 | Helped |
| Use implicit short-term correlation and long-term temporal refinement | SLT-Net · CAD | Sα: 0.655 -> 0.696 | Helped |
| Add the short-term module to the backbone (ablation) | SLT-Net · MoCA-Mask | Sα: 0.648 -> 0.662 | Helped |
| Add the long-term module to short-term SLT-Net (ablation) | SLT-Net · MoCA-Mask | Fβw: 0.350 -> 0.357 | Helped |
| Add the long-term module to short-term SLT-Net (ablation) | SLT-Net · MoCA-Mask | Sα: 0.662 -> 0.656 | Hurt |
| Replace ConvLSTM with a transformer for long-term refinement (ablation) | SLT-Net · MoCA-Mask | Fβw: 0.348 -> 0.357 | Helped |
| Replace ConvLSTM with a transformer for long-term refinement (ablation) | SLT-Net · MoCA-Mask | MAE: 0.021 -> 0.021 | No clear effect |
| Use a video input rather than a single-frame input (ablation) | SLT-Net · MoCA-Mask | Fβw: 0.330 -> 0.357 | Helped |
| Use a video input rather than a single-frame input (ablation) | RCRNet · MoCA-Mask | Fβw: 0.206 -> 0.204 | Hurt |
| Use the transformer long-term model instead of ConvLSTM (ablation) | SLT-Net · MoCA-Mask | parameter count: 179.03 MB -> 82.30 MB | Helped |
| Use COD10K pre-training (ablation) | SLT-Net · MoCA-Mask | Sα: 0.655 -> 0.656 | Helped |
| Add pseudo labels to video methods | Video-based methods · MoCA-Mask | Sα | Mixed |
Closest papers by meaning
- Endow SAM with Keen Eyes: Temporal-spatial Prompt Learning for Video Camouflaged Object Detection (CVPR 2024 · similarity 0.89)
- CamSAM2: Segment Anything Accurately in Camouflaged Videos (NeurIPS 2025 · similarity 0.88)
- CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models (AAAI 2024 · similarity 0.87)
- Shift the Lens: Environment-Aware Unsupervised Camouflaged Object Detection (CVPR 2025 · similarity 0.85)
- Camouflage Anything: Learning to Hide using Controlled Out-painting and Representation Engineering (CVPR 2025 · similarity 0.85)
- UCOD-DPL: Unsupervised Camouflaged Object Detection via Dynamic Pseudo-label Learning (CVPR 2025 · similarity 0.84)
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