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multimodal LSTM
A multimodal Long Short-Term Memory network is an extension of the recurrent neural network architecture designed to process, align, and integrate sequential information across multiple data modalities, such as text, images, and audio. While a standard Long Short-Term Memory unit tracks temporal dependencies within a single data stream using gated memory cells, a multimodal variant incorporates features from distinct modalities into its internal gating mechanisms and hidden state transitions to capture cross-modal relationships. This structure enables the network to learn shared representations and align fine-grained elements across modalities, such as linking sequential textual descriptions with visual regions or acoustic signals. Consequently, multimodal Long Short-Term Memory models are utilized in tasks requiring cross-modal reasoning and alignment, including image-text matching, image captioning, visual question answering, and audiovisual recognition.
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