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[CV] LookupViT: Compressing visual information to a limited number of tokens https://t.co/1aF8qB6nnA - LookupViT introduces a novel Multi-Head Bidirectional Cross-attention (MHBC) module that enables effective information flow with significant computational savings. -… https://t.co/t91frsNtNo
[CV] LookupViT: Compressing visual information to a limited number of tokens https://t.co/1aF8qB6nnA 英文版要点: - LookupViT introduces a novel Multi-Head Bidirectional Cross-attention (MHBC) module that enables effective information flow with significant computational… https://t.co/tALLB6HqCh
👀A sneak-peek at our recent work accepted at #ECCV2024 @eccvconf ! 📜: https://t.co/L3btjx1oSC Want a scalable and robust foundational model that also saves computational costs? Say hello to LookupViT, our information compression idea leading to sub-quadratic attention! (1/10) https://t.co/QLdxlDIGaB https://t.co/fYso3dNCFh
DeepMind has introduced a new method called LookupViT, which focuses on compressing visual information to a limited number of tokens. This method utilizes a novel Multi-Head Bidirectional Cross-attention (MHBC) module that significantly enhances computational efficiency. The research has been accepted at ECCV 2024, highlighting its potential for scalable and robust foundational models that save computational costs with sub-quadratic attention.
