Research map / Detection and segmentation / CNN features
RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add rotation-invariant feature learning | AlexNet fine-tuned for aerial car detection · Aerial car detection | AP: 90.2 -> 94.6 | Helped |
| Add rotation-invariant feature learning (ablation) | R-CNN with VGGNet & BB · PASCAL VOC 2007 test | mAP: 66.0 -> 66.7 | Helped |
| Add Fisher-discriminative feature learning (ablation) | R-CNN with VGGNet & BB · PASCAL VOC 2007 test | mAP: 66.0 -> 69.2 | Helped |
| Combine rotation-invariant and Fisher-discriminative feature learning (ablation) | R-CNN with VGGNet & BB · PASCAL VOC 2007 test | mAP: 66.0 -> 69.8 | Helped |
| Add fine-grained search to RIFD-CNN | RIFD-CNN · PASCAL VOC 2007 test | mAP: 69.8 -> 71.0 | Helped |
Closest papers by meaning
- Weakly Supervised Rotation-Invariant Aerial Object Detection Network (CVPR 2022 · similarity 0.89)
- R-FCN: Object Detection via Region-based Fully Convolutional Networks (NeurIPS 2016 · similarity 0.88)
- Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks (AAAI 2019 · similarity 0.86)
- Recurrently Aggregating Deep Features for Salient Object Detection (AAAI 2018 · similarity 0.86)
- SCNN: A General Distribution Based Statistical Convolutional Neural Network with Application to Video Object Detection (AAAI 2019 · similarity 0.86)
- FRED: Towards a Full Rotation-Equivariance in Aerial Image Object Detection (AAAI 2024 · similarity 0.85)
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