Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Механизм внимания× | Мультимодальный трансформер× | |
|---|---|---|
| Область | Глубокое обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2015 | 2019–2021 |
| Автор метода≠ | Bahdanau, D.; Luong, M.T. | Lu et al. (ViLBERT); Radford et al. (CLIP) |
| Тип≠ | Neural attention layer (encoder-decoder) | Cross-modal attention-based deep learning model |
| Основополагающий источник≠ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR. link ↗ | 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. link ↗ |
| Другие названия≠ | Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention | multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer |
| Связанные | 5 | 5 |
| Сводка≠ | The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector. | 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. |
| ScholarGateНабор данных ↗ |
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