Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Segmentación débilmente supervisada de instancias× | Segmentación Semántica Débilmente Supervisada× | |
|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2015–2019 | 2014–2016 |
| Autor original≠ | Multiple contributors (e.g., Hsu et al., Khoreva et al.) | Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational |
| Tipo≠ | Weakly supervised deep learning for pixel-wise instance delineation | Pixel-level classification with image-level or coarse supervision |
| Fuente seminal≠ | Hsu, C.-C., Hsu, K.-J., Tsai, C.-C., Lin, Y.-Y., & Chuang, Y.-Y. (2019). Weakly supervised instance segmentation using the bounding box tightness prior. Advances in Neural Information Processing Systems (NeurIPS), 32. link ↗ | Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning Deep Features for Discriminative Localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921–2929. DOI ↗ |
| Alias | WSIS, weakly-supervised mask prediction, weak-label instance segmentation, box-supervised instance segmentation | WSSS, weak-label segmentation, image-level supervised segmentation, weakly-labeled pixel classification |
| Relacionados≠ | 6 | 4 |
| Resumen≠ | Weakly supervised instance segmentation trains deep networks to delineate individual object instances at pixel level using only cheap, incomplete annotations — such as bounding boxes, image-level labels, or point clicks — rather than costly full pixel-wise masks. It dramatically reduces annotation effort while still producing instance-level masks for each object in an image. | Weakly Supervised Semantic Segmentation (WSSS) trains pixel-level scene parsers using only cheap, coarse annotations — typically image-level class tags — instead of costly dense pixel masks. By generating proxy pseudo-labels from a classification network (via Class Activation Maps or similar localisation cues) and iteratively refining them, WSSS brings full-supervision accuracy within reach at a fraction of the annotation cost. |
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