Linganisha mbinu

	Ugunduzi wa Kina wa Maelezo (Explainable Object Detection)	Uainishaji wa Matukio
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2016–2017	2017
Mwanzilishi	Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME); Lundberg & Lee (SHAP)	He, K., Gkioxari, G., Dollar, P., Girshick, R.
Aina	Post-hoc explainability applied to object detection	Pixel-level detection and mask prediction
Chanzo asilia	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.
Majina mbadala	XAI Object Detection, Interpretable Object Detection, Transparent Object Detection, Explainable OD	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
Zinazohusiana	5	4
Muhtasari	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.