方法对比
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| 弱监督目标检测× | 图像分类× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2016 (deep WSOD); MIL roots circa 1997 | 2012 (deep CNN era); conceptual roots 1989 (LeCun) |
| 提出者≠ | Bilen, H. & Vedaldi, A. (WSDDN); Multiple Instance Learning origins: Dietterich et al. (1997) | Krizhevsky, A.; Sutskever, I.; Hinton, G. E. |
| 类型≠ | Weakly supervised detection paradigm | Supervised classification task |
| 开创性文献≠ | 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 ↗ | Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105. link ↗ |
| 别名 | WSOD, weakly-supervised detection, image-level supervised detection, multiple instance detection | visual classification, image recognition, CNN-based classification, visual categorization |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles. |
| ScholarGate数据集 ↗ |
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