Linganisha mbinu

	Utambuzi wa vitu vingi (Multimodal Object Detection)	Transformeri wa Multimodal
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2015–2019	2019–2021
Mwanzilishi	Multiple contributors (e.g., Chen & Deng, Liang et al.)	Lu et al. (ViLBERT); Radford et al. (CLIP)
Aina	Fusion-based deep detection	Cross-modal attention-based deep learning model
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.	Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32.
Majina mbadala	multi-sensor object detection, cross-modal detection, RGB-D object detection, fusion-based object detection	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Zinazohusiana	6	5
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.	A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis.