方法对比
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| 弱监督实例分割× | 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≠ | 2015–2019 | 2014–2016 |
| 提出者≠ | Multiple contributors (e.g., Hsu et al., Khoreva et al.) | Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO) |
| 类型≠ | Weakly supervised deep learning for pixel-wise instance delineation | Supervised deep learning (region proposal or single-shot) |
| 开创性文献≠ | 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 ↗ | 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 | WSIS, weakly-supervised mask prediction, weak-label instance segmentation, box-supervised instance segmentation | visual object detection, image object localization, region-based object detection, bounding-box detection |
| 相关≠ | 6 | 3 |
| 摘要≠ | 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. | 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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