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× | ビジョントランスフォーマー× | |
|---|---|---|
| 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 | 2021 |
| 提唱者≠ | Bilen, H. & Vedaldi, A. (WSDDN); Multiple Instance Learning origins: Dietterich et al. (1997) | Dosovitskiy, A. et al. |
| 種類≠ | Weakly supervised detection paradigm | Transformer architecture for images (self-attention over patches) |
| 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 ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| 別名 | WSOD, weakly-supervised detection, image-level supervised detection, multiple instance detection | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| 関連 | 5 | 5 |
| 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. | The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs). |
| ScholarGateデータセット ↗ |
|
|