Research map / Detection and segmentation / Open-set and few-shot detection
OW-DETR: Open-World Detection Transformer
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace ORE − EBUI with OW-DETR | MS-COCO OWOD · MS-COCO Task 1 | U-Recall: 4.9 -> 7.5 | Helped |
| Replace ORE − EBUI with OW-DETR | MS-COCO OWOD · MS-COCO Task 2 | U-Recall: 2.9 -> 6.2 | Helped |
| Replace ORE − EBUI with OW-DETR | MS-COCO OWOD · MS-COCO Task 3 | U-Recall: 3.9 -> 5.7 | Helped |
| Add novelty classification trained with attention-selected pseudo-unknowns (ablation) | Deformable DETR baseline · MS-COCO Task 1 | U-Recall: no unknown detection -> 5.9 | Helped |
| Add novelty classification trained with attention-selected pseudo-unknowns (ablation) | Deformable DETR baseline · MS-COCO Task 2 | U-Recall: 4.5 -> 4.6 | Helped |
| Add novelty classification trained with attention-selected pseudo-unknowns (ablation) | Deformable DETR baseline · MS-COCO Task 3 | U-Recall: 3.3 -> 4.6 | Helped |
| Add foreground objectness branch (ablation) | Baseline + NC · MS-COCO Task 1 | U-Recall: 5.9 -> 7.5 | Helped |
| Add foreground objectness branch (ablation) | Baseline + NC · MS-COCO Task 2 | U-Recall: 4.6 -> 6.2 | Helped |
| Add foreground objectness branch (ablation) | Baseline + NC · MS-COCO Task 3 | U-Recall: 4.6 -> 5.7 | Helped |
| Use self-supervised DINO rather than fully-supervised ImageNet pretraining (ablation) | OW-DETR with ResNet-50 · MS-COCO Task 1 | U-Recall: 6.2 -> 7.5 | Helped |
Closest papers by meaning
- Semi-supervised Open-World Object Detection (AAAI 2024 · similarity 0.91)
- OW-OVD: Unified Open World and Open Vocabulary Object Detection (CVPR 2025 · similarity 0.91)
- UMB: Understanding Model Behavior for Open-World Object Detection (NeurIPS 2024 · similarity 0.91)
- Open-World Objectness Modeling Unifies Novel Object Detection (CVPR 2025 · similarity 0.90)
- OW-VAP: Visual Attribute Parsing for Open World Object Detection (ICML 2025 · similarity 0.90)
- Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detection (AAAI 2024 · similarity 0.90)
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