natural-language-processing-5
Multimodal Deep Learning
3260 papers • 126 benchmarks • 313 datasets
Multimodal deep learning is a type of deep learning that combines information from multiple modalities, such as text, image, audio, and video, to make more accurate and comprehensive predictions. It involves training deep neural networks on data that includes multiple types of information and using the network to make predictions based on this combined data. One of the key challenges in multimodal deep learning is how to effectively combine information from multiple modalities. This can be done using a variety of techniques, such as fusing the features extracted from each modality, or using attention mechanisms to weight the contribution of each modality based on its importance for the task at hand. Multimodal deep learning has many applications, including image captioning, speech recognition, natural language processing, and autonomous vehicles. By combining information from multiple modalities, multimodal deep learning can improve the accuracy and robustness of models, enabling them to perform better in real-world scenarios where multiple types of information are present.
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CUB-200-2011
CUB-200-2011
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LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
Hongsheng Li, Pan Lu, Renrui Zhang, Shilin Yan, Peng Gao, Y. Qiao, Jiaming Han, Aojun Zhou, Xiangfei Hu•Mon Mar 27 2023
A zero-initialized attention mechanism with zero gating is proposed, which adaptively injects the new instructional cues into LLaMA, while effectively preserves its pre-trained knowledge on traditional vision and language tasks, demonstrating the superior generalization capacity of the approach.
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