CVA aggregates frozen VFM embeddings via latent reasoning to create compact video embeddings for efficient micro-video recommendation, delivering consistent performance gains and orders-of-magnitude efficiency improvements.
Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
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Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation
CVA aggregates frozen VFM embeddings via latent reasoning to create compact video embeddings for efficient micro-video recommendation, delivering consistent performance gains and orders-of-magnitude efficiency improvements.