Object Detection on PASCAL VOC 2012 (train val)
73.4CorLocOurs
Evaluation results
| Method | Links | ||
|---|---|---|---|
| OursBackbone=VGG16, Pre-trained=ImageNet, Proposal generation=Selective Search, Input scale={480, 576, 688, 864, 1000, 1200}2025.05 | 73.4 | — | |
| CBL2025.05 | 72.6 | — | |
| CASD2025.05 | 72.3 | — | |
| NDI-MIL2025.05 | 72.2 | — | |
| WSOD22025.05 | 71.9 | — | |
| C-MIDN2025.05 | 71.2 | — | |
| ODCL2025.05 | 71.2 | — | |
| MIST2025.05 | 70.9 | — | |
| IM-CFB2025.05 | 69.6 | — | |
| Yang et al.2025.05 | 69.5 | — | |
| SLV2025.05 | 69.2 | — | |
| C-MIL2025.05 | 67.4 | — | |
| OICR2025.05 | 52.1 | — | |
| MELMCNN=VGGF/AlexNet, Dataset Splitting=train/val2019.02 | — | 36.2 | |
| MELMCNN=VGG16, Dataset Splitting=train/val2019.02 | — | 40.2 | |
| MILinearCNN=VGGF/AlexNet, Dataset Splitting=train/val2019.02 | — | 23.8 | |
| PDACNN=VGGF/AlexNet, Dataset Splitting=train/val2019.02 | — | 22.4 | |
| PDACNN=VGG16, Dataset Splitting=train/val2019.02 | — | 29.1 | |
| Self-TaughtCNN=VGG16, Dataset Splitting=train/val2019.02 | — | 39 |