They observed that applying two Understanding targets balances the model’s learning course of action, which allows CAV-MAE to understand the corresponding audio and visual info even though improving upon its ability to recover video clips that match user queries.
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In their enhanced product, named CAV-MAE Sync, the researchers break up the audio into smaller sized windows before the product computes its representations of the information, so it generates separate representations that correspond to each smaller sized window of audio.
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MIT News Publication Day: May 22, 2025
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