MetaFormer, the abstracted architecture of Transformer, has been found to play a significant role in achieving competitive performance. In this paper, we further explore the capacity of MetaFormer, again, by migrating our focus away from the token mixer design: we introduce several baseline models under MetaFormer using the most basic or common mixers, and demonstrate their gratifying performance. We summarize our observations as follows: 1)MetaFormer ensures solid lower bound of performance: By merely adopting identity mapping as the token mixer, the MetaFormer model, termed IdentityFormer, achieves $> $$>$80% accuracy on ImageNet-1 K. 2)MetaFormer works well with arbitrary token mixers: When specifying the token mixer as even a random matrix to mix tokens, the resulting model RandFormer yields an accuracy of $> $$>$81%, outperforming IdentityFormer. Rest assured of MetaFormer's results when new token mixers are adopted. 3)MetaFormer effortlessly offers state-of-the-art results: With just conventional token mixers dated back five years ago, the models instantiated from MetaFormer already beat state of the art. a)ConvFormer outperforms ConvNeXt: Taking the common depthwise separable convolutions as the token mixer, the model termed ConvFormer, which can be regarded as pure CNNs, outperforms the strong CNN model ConvNeXt. b)CAFormer sets new record on ImageNet-1 K: By simply applying depthwise separable convolutions as token mixer in the bottom stages and vanilla self-attention in the top stages, the resulting model CAFormer sets a new record on ImageNet-1 K: it achieves an accuracy of 85.5% at $224 \times 224$$224\times 224$ resolution, under normal supervised training without external data or distillation. In our expedition to probe MetaFormer, we also find that a new activation, StarReLU , reduces 71% FLOPs of activation compared with commonly-used GELU yet achieves better performance. Specifically, StarReLU is a variant of Squared ReLU dedicated to alleviating distribution shift. We expect StarReLU to find great potential in MetaFormer-like models alongside other neural networks.

Prior research on the impact of real estate investment trust (REIT) ownership on property performance is very limited and provides inconclusive empirical evidence. Whether REITs add value at the micro-level remains a puzzle. Utilizing a dataset of detailed accounting information for individual hotels across five states in the United States, we re-examine the performance of REIT-owned properties. Unlike prior research that focuses on revenue-based performance measures, we examine both the top-line and bottom-line performance of hotel operations. We find that REIT ownership favorably impacts property performance in that REIT-owned hotels have higher profit margins than other lodging properties. The greater cost efficiency is likely attributable to savings in non-distributed operating expenses and fixed charges. We document no outperformance by REITs in revenue growth at the individual property level.