Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Detección de Objetos Débilmente Supervisada× | Vision Transformer× | |
|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2016 (deep WSOD); MIL roots circa 1997 | 2021 |
| Autor original≠ | Bilen, H. & Vedaldi, A. (WSDDN); Multiple Instance Learning origins: Dietterich et al. (1997) | Dosovitskiy, A. et al. |
| Tipo≠ | Weakly supervised detection paradigm | Transformer architecture for images (self-attention over patches) |
| Fuente seminal≠ | 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 ↗ |
| Alias | WSOD, weakly-supervised detection, image-level supervised detection, multiple instance detection | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Relacionados | 5 | 5 |
| Resumen≠ | 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). |
| ScholarGateConjunto de datos ↗ |
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