So sánh phương pháp

	Phát hiện đối tượng yếu giám sát	Phát hiện đối tượng
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2016 (deep WSOD); MIL roots circa 1997	2014–2016
Người khởi xướng	Bilen, H. & Vedaldi, A. (WSDDN); Multiple Instance Learning origins: Dietterich et al. (1997)	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)
Loại	Weakly supervised detection paradigm	Supervised deep learning (region proposal or single-shot)
Công trình gốc	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.	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.
Tên gọi khác	WSOD, weakly-supervised detection, image-level supervised detection, multiple instance detection	visual object detection, image object localization, region-based object detection, bounding-box detection
Liên quan	5	3
Tóm tắt	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.	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.