So sánh phương pháp

	Nhận diện đối tượng có thể giải thích	Phân đoạn thực thể (Instance Segmentation)
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2016–2017	2017
Người khởi xướng	Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME); Lundberg & Lee (SHAP)	He, K., Gkioxari, G., Dollar, P., Girshick, R.
Loại	Post-hoc explainability applied to object detection	Pixel-level detection and mask prediction
Công trình gốc	Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618–626.	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.
Tên gọi khác	XAI Object Detection, Interpretable Object Detection, Transparent Object Detection, Explainable OD	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
Liên quan	5	4
Tóm tắt	Explainable object detection combines a deep-learning object detector — such as YOLO, Faster R-CNN, or DETR — with post-hoc or built-in explainability methods (Grad-CAM, LIME, SHAP, D-RISE) that visualize why the model placed a bounding box at a particular location and assigned a particular class label, making its decisions auditable by humans.	Instance segmentation is a computer vision task that simultaneously detects every distinct object in an image and produces a precise pixel-level mask for each individual object instance. Unlike semantic segmentation, which labels every pixel with a class, instance segmentation distinguishes between separate objects of the same class, enabling fine-grained spatial understanding.