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co-attention mechanism
A co-attention mechanism is a deep learning technique that jointly and interactively computes attention representations across two distinct input sequences or modalities, allowing each source to guide the feature selection of the other. Unlike standard attention models that operate in a single direction by using one input to attend to elements of another, co-attention enables bidirectional, symmetric reasoning by capturing mutual dependencies between both sources simultaneously. This reciprocal interaction allows neural networks to align and highlight the most relevant components across both inputs, such as identifying key regions in an image while simultaneously focusing on the most pertinent words in a text query, which enhances performance in multimodal tasks like visual question answering and text matching.
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