Applying Transformers to irregular time-series typically requires specializations to their baseline architecture, which can result in additional computational overhead and increased method complexity. We present the Rotary Masked Autoencoder (RoMAE), which utilizes the popular Rotary Positional Embedding (RoPE) method for continuous positions. RoMAE is an extension to the Masked Autoencoder (MAE) that enables interpolation and representation learning with multidimensional continuous positional information while avoiding any time-series-specific architectural specializations.

As action decoder their mentioned architectures of is multimodal adopted in the in to paper Figure information generate, the 1. visual-gr natural with languages cross-attention ounded alignment conditioned blocks, decoder on while the is visual applied the visual-grounded input. Based on these deeply fused representations, we finally generate the predicted answers with the visual-grounded generation decoder. In this section, we describe the settings used when fine-tuning the pretrained models on various downstream tasks. We use RandomAugment [1] for data augmentation. The default settings for finetuning on each dataset are shown in Table 1.

D.2 Countries Hyperparameters are summarized in table 6. We ran all experiments on a single CPU (Apple M2). 15 optimizer AdamW learning rate 0.0003 learning rate schedule cosine training epochs 100 weight decay 0.00001 batch size 4 embedding dimensions 10 embedding initialization one-hot, fixed neural networks LeNet5 max search depth / Table 5: Hyperparameters for the MNIST -addition experiments.

We use the same setting for different sizes RevCol models on MIM pre-training. The hyper-parameters generally follow [4, 2]. Table 3 shows the detail training settings after MIM pre-training. We also show training settings on ImageNet-1K after ImageNet-22K fine-tuning. For semantic segmentation, we evaluate different backbones on ADE20K dataset.