Research map / Detection and segmentation / Open-set and few-shot detection
Taking a Deeper Look at Co-Salient Object Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SOD models rather than CoSOD-specific models | CoSOD detection · iCoSeg and CoSal2015 | Sα: iCoSeg: .861 (CPD) -> .875 (EGNet); CoSal2015: .814 (CPD) -> .818 (EGNet) | Helped |
| Use superpixel-based methods on appearance-similar groups | CoSOD methods · MSRC | Sα: UMLF: .797; GONet: .795 | Helped |
| Rely on superpixel techniques for semantic-aware co-saliency | CoSOD methods · iCoSeg and CoSOD3k | Sα: GONet: No. 2 → No. 5 | Hurt |
| Use SOD models without modeling corresponding objects across images | CoSOD detection · CoSOD3k | Sα: EGNet: .762; CPD: .757 | Mixed |
Closest papers by meaning
- CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection (NeurIPS 2020 · similarity 0.92)
- Co-Salient Object Detection With Uncertainty-Aware Group Exchange-Masking (CVPR 2023 · similarity 0.91)
- Visual Consensus Prompting for Co-Salient Object Detection (CVPR 2025 · similarity 0.90)
- CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection (CVPR 2024 · similarity 0.90)
- DiMSOD: A Diffusion-Based Framework for Multi-Modal Salient Object Detection (AAAI 2025 · similarity 0.89)
- Alignment-Free RGB-T Salient Object Detection: A Large-Scale Dataset and Progressive Correlation Network (AAAI 2025 · similarity 0.87)
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