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| Erklärbare Objekterkennung× | Instanzsegmentierung× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2016–2017 | 2017 |
| Urheber≠ | Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME); Lundberg & Lee (SHAP) | He, K., Gkioxari, G., Dollar, P., Girshick, R. |
| Typ≠ | Post-hoc explainability applied to object detection | Pixel-level detection and mask prediction |
| Wegweisende Quelle≠ | 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. DOI ↗ | He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969. DOI ↗ |
| Aliasnamen | XAI Object Detection, Interpretable Object Detection, Transparent Object Detection, Explainable OD | instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | 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. |
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