Research map / Detection and segmentation / Open-set and few-shot detection
Objects in Semantic Topology
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| constrain instance features to fixed semantic anchors, including an unknown anchor | Faster R-CNN open-world detector · Open-world detection, Task 2 | A-OSE: 7832 -> 2546 | Helped |
| constrain instance features to fixed semantic anchors, including an unknown anchor | Faster R-CNN open-world detector · Open-world detection, Task 3 | mAP, both known: 28.82 -> 29.63 | Helped |
| use randomly sampled anchors instead of CLIP text anchors (ablation) | Semantic Topology detector · Open-world detection, Task 2 | A-OSE: 2546 -> 2779 | Hurt |
| use BERT text embeddings instead of CLIP text embeddings (ablation) | Semantic Topology detector · Open-world detection, Task 2 | A-OSE: 2546 -> 2772 | Hurt |
| add semantic anchor clustering loss (ablation) | Semantic Topology detector · Open-world detection, Task 2 | A-OSE: 12564 -> 2546 | Helped |
| add the unknown-class semantic anchor (ablation) | Semantic Topology detector · Open-world detection, Task 2 | A-OSE: 3354 -> 2546 | Helped |
| add semantic feature classification loss (ablation) | Semantic Topology detector · Open-world detection, Task 2 | A-OSE: 11297 -> 2546 | Helped |
| add RoI feature classification loss (ablation) | Semantic Topology detector · Open-world detection, Task 2 | A-OSE: 2896 -> 2546 | Helped |
| replace language-model anchors with random anchors (ablation) | Semantic Topology detector · Open-world detection, Task 2 | A-OSE versus ORE: 7832 -> 2779 | Helped |
| use semantic anchors to stabilize features during incremental learning | Semantic Topology detector · Class-incremental detection, 15 + 5 | mAP, both classes versus ORE: 68.17 -> 69.93 | Helped |
Closest papers by meaning
- The Devil is in the Fine-Grained Details: Evaluating Open-Vocabulary Object Detectors for Fine-Grained Understanding (CVPR 2024 · similarity 0.88)
- GCD: Advancing Vision-Language Models for Incremental Object Detection via Global Alignment and Correspondence Distillation (AAAI 2025 · similarity 0.88)
- Generative Region-Language Pretraining for Open-Ended Object Detection (CVPR 2024 · similarity 0.88)
- Recognize Any Regions (NeurIPS 2024 · similarity 0.87)
- VL-SAM-V2: Open-World Object Detection with General and Specific Query Fusion (NeurIPS 2025 · similarity 0.87)
- Exploring Orthogonality in Open World Object Detection (CVPR 2024 · similarity 0.87)
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