Сравнение методов

	Мультимодальная классификация изображений	Мультимодальное обнаружение объектов
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2011–2021	2015–2019
Автор метода	Ngiam et al.; Radford et al. (CLIP)	Multiple contributors (e.g., Chen & Deng, Liang et al.)
Тип	Multimodal supervised classification	Fusion-based deep detection
Основополагающий источник	Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139, 8748–8763.	Liu, Y., Zhang, F., Li, Y., & Lv, H. (2022). Multimodal Object Detection via Bayesian Fusion. IEEE Transactions on Image Processing, 31, 5953–5965.
Другие названия	multimodal visual classification, image-text classification, vision-language classification, cross-modal image classification	multi-sensor object detection, cross-modal detection, RGB-D object detection, fusion-based object detection
Связанные	6	6
Сводка	Multimodal image classification extends standard visual classification by incorporating additional modalities — such as text captions, audio, or structured metadata — alongside image features. Separate encoders process each modality, their representations are fused, and a joint classifier assigns the target label. Models such as CLIP demonstrate that image–text alignment enables zero-shot and few-shot image classification at scale.	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.