データ拡張 (Data Augmentation) を学習中に使い分けるRefined Data Augmentationについて解説しました。
He, Zhuoxun, et al. "Data augmentation revisited: Rethinking the distribution gap between clean and augmented data." arXiv preprint arXiv:1909.09148 (2019).
データ拡張 (Data Augmentation) を学習中に使い分けるRefined Data Augmentationについて解説しました。
He, Zhuoxun, et al. "Data augmentation revisited: Rethinking the distribution gap between clean and augmented data." arXiv preprint arXiv:1909.09148 (2019).
[DL輪読会]Domain Adaptive Faster R-CNN for Object Detection in the WildDeep Learning JP
The document discusses domain adaptive faster R-CNN for object detection. It proposes a method to adapt a model trained on labeled data from a source domain to detect objects in an unlabeled target domain. The method uses an end-to-end deep learning model with two stages. First, it reduces differences in image distributions between the source and target domains. Then it performs object detection on the target domain images using the adapted model.
[DL輪読会]Domain Adaptive Faster R-CNN for Object Detection in the WildDeep Learning JP
The document discusses domain adaptive faster R-CNN for object detection. It proposes a method to adapt a model trained on labeled data from a source domain to detect objects in an unlabeled target domain. The method uses an end-to-end deep learning model with two stages. First, it reduces differences in image distributions between the source and target domains. Then it performs object detection on the target domain images using the adapted model.
16. 16
Girshick, R., Donahue, J., Darrell, T., & Malik, J. “Rich feature hierarchies for accurate object detection and semantic segmentation”. CVPR2014.
R-CNN: 複数カテゴリへの対応
物体候補を抜き出しそれぞれをCNNにかける
CNNの結果(Score)を元にオブジェクトを判別
(元はSVMで判定. 今回は前述のロジックで判別)
17. 17
Jasper R. R. Uijlings, Koen E. A. van de Sande, Theo Gevers, Arnold W. M. Smeulders “Selective Search for Object Recognition”
Selective Search:物体候補の抜き出し
類似した領域をグルーピングして物体候補を抽出す
るアルゴリズム
DeepLearningと違って学習の必要がない