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Keyword: ConvNeXt V2 ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

   Woo, Debnath, Hu et al. · arXiv (Cornell University) · 2023

   The paper proposes ConvNeXt V2, which co-designs a fully convolutional masked autoencoder with a new Global Response Normalization layer to improve pure ConvNets on image recognition benchmarks.

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   1. UncheckedThe authors report that simply pairing ConvNeXt with masked-autoencoder pre-training gives worse results than expected.“However, we found that simply combining these two approaches leads to subpar performance.”
   2. UncheckedCombining self-supervised masked pre-training with a new layer in ConvNeXt gives ConvNeXt V2, which the paper says markedly improves pure ConvNets on several benchmarks.“This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmen…”