方法对比
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| 弱监督语义分割× | 目标检测× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份 | 2014–2016 | 2014–2016 |
| 提出者≠ | Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational | Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO) |
| 类型≠ | Pixel-level classification with image-level or coarse supervision | Supervised deep learning (region proposal or single-shot) |
| 开创性文献≠ | 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 ↗ | 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 ↗ |
| 别名 | WSSS, weak-label segmentation, image-level supervised segmentation, weakly-labeled pixel classification | visual object detection, image object localization, region-based object detection, bounding-box detection |
| 相关≠ | 4 | 3 |
| 摘要≠ | 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. | 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数据集 ↗ |
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