Linganisha mbinu

	Utambuzi wa vitu vingi (Multimodal Object Detection)	Uainishaji wa Picha wa Multimodal
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2015–2019	2011–2021
Mwanzilishi	Multiple contributors (e.g., Chen & Deng, Liang et al.)	Ngiam et al.; Radford et al. (CLIP)
Aina	Fusion-based deep detection	Multimodal supervised classification
Chanzo asilia	Liu, Y., Zhang, F., Li, Y., & Lv, H. (2022). Multimodal Object Detection via Bayesian Fusion. IEEE Transactions on Image Processing, 31, 5953–5965.	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.
Majina mbadala	multi-sensor object detection, cross-modal detection, RGB-D object detection, fusion-based object detection	multimodal visual classification, image-text classification, vision-language classification, cross-modal image classification
Zinazohusiana	6	6
Muhtasari	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.	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.