手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 弱教師あり物体検出× | 物体検出× | |
|---|---|---|
| 分野 | 深層学習 | 深層学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2016 (deep WSOD); MIL roots circa 1997 | 2014–2016 |
| 提唱者≠ | Bilen, H. & Vedaldi, A. (WSDDN); Multiple Instance Learning origins: Dietterich et al. (1997) | Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO) |
| 種類≠ | Weakly supervised detection paradigm | Supervised deep learning (region proposal or single-shot) |
| 原典≠ | Bilen, H., & Vedaldi, A. (2016). Weakly supervised deep detection networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2846–2854. DOI ↗ | Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587. DOI ↗ |
| 別名 | WSOD, weakly-supervised detection, image-level supervised detection, multiple instance detection | visual object detection, image object localization, region-based object detection, bounding-box detection |
| 関連≠ | 5 | 3 |
| 概要≠ | Weakly Supervised Object Detection (WSOD) trains object detectors using only image-level labels — indicating which object classes appear in an image — without requiring costly bounding-box annotations. Multiple Instance Learning (MIL) formulations allow the model to discover the likely location of each object class from classification signals alone, dramatically reducing annotation cost. | Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks. |
| ScholarGateデータセット ↗ |
|
|