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	Detekcja obiektów multimodalnych	Klasyfikacja obrazów
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2015–2019	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Twórca	Multiple contributors (e.g., Chen & Deng, Liang et al.)	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.
Typ	Fusion-based deep detection	Supervised classification task
Źródło pierwotne	Liu, Y., Zhang, F., Li, Y., & Lv, H. (2022). Multimodal Object Detection via Bayesian Fusion. IEEE Transactions on Image Processing, 31, 5953–5965.	Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105.
Inne nazwy	multi-sensor object detection, cross-modal detection, RGB-D object detection, fusion-based object detection	visual classification, image recognition, CNN-based classification, visual categorization
Pokrewne	6	5
Podsumowanie	Multimodal object detection extends single-modality object detectors by jointly processing signals from multiple sensor types — such as RGB cameras, depth sensors, LiDAR, radar, or text descriptions — to localize and classify objects with higher accuracy and robustness than any single modality alone. Fusion of complementary information is the core design principle.	Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles.