So sánh phương pháp

	Phân loại ảnh đa phương thức	Phát hiện đối tượng đa phương thức
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2011–2021	2015–2019
Người khởi xướng	Ngiam et al.; Radford et al. (CLIP)	Multiple contributors (e.g., Chen & Deng, Liang et al.)
Loại	Multimodal supervised classification	Fusion-based deep detection
Công trình gốc	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.
Tên gọi khác	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
Liên quan	6	6
Tóm tắt	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.