mc-BEiT: Multi-choice Discretization for Image BERT Pre-training

• URL: http://arxiv.org/abs/2203.15371v1
• Date: Tue, 29 Mar 2022 09:08:18 GMT
• Title: mc-BEiT: Multi-choice Discretization for Image BERT Pre-training
• Authors: Xiaotong Li, Yixiao Ge, Kun Yi, Zixuan Hu, Ying Shan, Ling-Yu Duan
• Abstract summary: Image BERT pre-training with masked image modeling (MIM) is a popular practice to cope with self-supervised representation learning. We introduce an improved BERT-style image pre-training method, namely mc-BEiT, which performs MIM proxy tasks towards eased and refined multi-choice training objectives.
• Score: 52.04866462439979
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image BERT pre-training with masked image modeling (MIM) becomes a popular practice to cope with self-supervised representation learning. A seminal work, BEiT, casts MIM as a classification task with a visual vocabulary, tokenizing the continuous visual signals into discrete vision tokens using a pre-learned dVAE. Despite a feasible solution, the improper discretization hinders further improvements of image pre-training. Since image discretization has no ground-truth answers, we believe that the masked patch should not be assigned with a unique token id even if a better tokenizer can be obtained. In this work, we introduce an improved BERT-style image pre-training method, namely mc-BEiT, which performs MIM proxy tasks towards eased and refined multi-choice training objectives. Specifically, the multi-choice supervision for the masked image patches is formed by the soft probability vectors of the discrete token ids, which are predicted by the off-the-shelf image tokenizer and further refined by high-level inter-patch perceptions resorting to the observation that similar patches should share their choices. Extensive experiments on classification, segmentation, and detection tasks demonstrate the superiority of our method, e.g., the pre-trained ViT-B achieves 84.1% top-1 fine-tuning accuracy on ImageNet-1K classification, 51.2% mIOU on ADE20K semantic segmentation, 51.2% AP^b and 44.3% AP^m of object detection and instance segmentation on COCO, outperforming the competitive counterparts.

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