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Status: Unchecked ×Keyword: long-range dependencies ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   UniFormer: Unifying Convolution and Self-Attention for Visual Recognition

   Li, Wang, Zhang et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2023

   The paper proposes UniFormer, a vision backbone combining convolution and self-attention, and reports strong results on image, video and dense prediction tasks, including a faster, more efficient variant.

   Unchecked3 claims
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   1. UncheckedThe UniFormer model reaches 86.3 top-1 accuracy on ImageNet-1K image classification without any extra training data.“Without any extra training data, our UniFormer achieves 86.3 top-1 accuracy on ImageNet-1K classification.”
   2. UncheckedWith only ImageNet-1K pre-training, UniFormer is reported to reach state-of-the-art results across video, detection, segmentation and pose tasks.“With only ImageNet-1K pre-training, it can simply achieve state-of-the-art performance in a broad range of downstream tasks, e.g., it obtains 82.9/84.8 top-1 accuracy on Kinetics-400/600, 60.9/71.2 top-1 accuracy on Sth-Sth V1/V2 video classification, 53.8 box AP and 46.4 mask AP on COCO object det…”
   3. UncheckedThe authors report building an efficient version of UniFormer that has 2-4 times higher throughput.“We further build an efficient UniFormer with 2-4x higher throughput.”