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Google DeepMind has introduced the Mixture of Nested Experts (MoNE), a novel framework designed to improve the efficiency of visual token processing. The MoNE framework dynamically selects redundant tokens, which allows it to achieve baseline performance with more than a two-fold reduction in inference time. This adaptive processing method prioritizes tokens, enabling cheaper nested experts to handle redundant information. The paper detailing this framework, authored by G Jain, N Hegde, A Kusupati, and A Nagrani, highlights the significant advancements in computational resource allocation for visual tokens. The visual medium, including images and videos, naturally contains a large amount of information redundancy, providing an opportunity for leveraging efficiency in processing. The announcement was made on July 30, 2024.
New research introduces MoMa, a modality-aware mixture-of-experts architecture, achieving 3.7x overall pre-training efficiency gains in mixed-modal language models over dense baselines, with even greater improvements when combined with mixture-of-depths.: https://t.co/P5V4cOZasy https://t.co/7JxBxbPqBx
Google DeepMind Presents MoNE: A Novel Computer Vision Framework for the Adaptive Processing of Visual Tokens by Dynamically Allocating Computational Resources to Different Tokens #DL #AI #ML #DeepLearning #ArtificialIntelligence #ComputerVision https://t.co/njnySxvgj4
From Images to Insights: DeepMind’s Versatile Vision-Language Model PaliGemma Achieves SOTA Results | Synced #DL #AI #ML #DeepLearning #ArtificialIntelligence #MachineLearning #ComputerVision #AutonomousVehicles #NeuroMorphic #Robotics https://t.co/Vkuk8B8Wn2
