Polyp segmentation has recently garnered significant attention, and multiple
methods have been formulated to achieve commendable outcomes. However, these
techniques often confront difficulty when working with the complex polyp
foreground and their surrounding regions because of the nature of convolution
operation. Besides, most existing methods forget to exploit the potential
information from multiple decoder stages. To address this challenge, we suggest
combining MetaFormer, introduced as a baseline for integrating CNN and
Transformer, with UNet framework and incorporating our Multi-scale Upsampling
block (MU). This simple module makes it possible to combine multi-level
information by exploring multiple receptive field paths of the shallow decoder
stage and then adding with the higher stage to aggregate better feature
representation, which is essential in medical image segmentation. Taken all
together, we propose MetaFormer Multi-scale Upsampling Network (M$^2$UNet) for
the polyp segmentation task. Extensive experiments on five benchmark datasets
demonstrate that our method achieved competitive performance compared with
several previous methods.

本文提出了一种基于 MetaFormer、UNet 和多尺度上采样块的多尺度上采样网络（M$^2$UNet）用于息肉分割任务，该方法在多个基准数据集上都与先前的方法相比具有竞争性的表现。

M^2UNet: 元 - 变形 - 多尺度上采样网络用于息肉分割

M^2UNet: MetaFormer Multi-scale Upsampling Network for Polyp  Segmentation

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, without focusing on token
mixer design: we introduce several baseline models under MetaFormer using the
most basic or common mixers, and 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-1K. (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-1K. 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-1K:
it achieves an accuracy of 85.5% at 224x224 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 GELU yet achieves better performance. We expect
StarReLU to find great potential in MetaFormer-like models alongside other
neural networks.

本文基于 MetaFormer 架构探索了不同的 token mixer 的组合方式，取得了较高的图像分类准确率，同时也提出了新的激活函数 StarReLU。

用于视觉的 MetaFormer 基线

MetaFormer Baselines for Vision

Fine-Grained Visual Classification(FGVC) is the task that requires
recognizing the objects belonging to multiple subordinate categories of a
super-category. Recent state-of-the-art methods usually design sophisticated
learning pipelines to tackle this task. However, visual information alone is
often not sufficient to accurately differentiate between fine-grained visual
categories. Nowadays, the meta-information (e.g., spatio-temporal prior,
attribute, and text description) usually appears along with the images. This
inspires us to ask the question: Is it possible to use a unified and simple
framework to utilize various meta-information to assist in fine-grained
identification? To answer this problem, we explore a unified and strong
meta-framework(MetaFormer) for fine-grained visual classification. In practice,
MetaFormer provides a simple yet effective approach to address the joint
learning of vision and various meta-information. Moreover, MetaFormer also
provides a strong baseline for FGVC without bells and whistles. Extensive
experiments demonstrate that MetaFormer can effectively use various
meta-information to improve the performance of fine-grained recognition. In a
fair comparison, MetaFormer can outperform the current SotA approaches with
only vision information on the iNaturalist2017 and iNaturalist2018 datasets.
Adding meta-information, MetaFormer can exceed the current SotA approaches by
5.9% and 5.3%, respectively. Moreover, MetaFormer can achieve 92.3% and 92.7%
on CUB-200-2011 and NABirds, which significantly outperforms the SotA
approaches. The source code and pre-trained models are released
atthis https URL

本文介绍了 MetaFormer 方法，它使用统一和简单的框架利用各种元信息来协助细粒度图像识别。实验证明，MetaFormer 可以有效地使用各种元信息来提高细粒度识别的性能，并且在 iNaturalist2017 和 iNaturalist2018 数据集上可以优于目前的最先进方法。

MetaFormer: 一种用于细粒度识别的统一元框架

MetaFormer: A Unified Meta Framework for Fine-Grained Recognition

Transformers have shown great potential in computer vision tasks. A common
belief is their attention-based token mixer module contributes most to their
competence. However, recent works show the attention-based module in
Transformers can be replaced by spatial MLPs and the resulted models still
perform quite well. Based on this observation, we hypothesize that the general
architecture of the Transformers, instead of the specific token mixer module,
is more essential to the model's performance. To verify this, we deliberately
replace the attention module in Transformers with an embarrassingly simple
spatial pooling operator to conduct only basic token mixing. Surprisingly, we
observe that the derived model, termed as PoolFormer, achieves competitive
performance on multiple computer vision tasks. For example, on ImageNet-1K,
PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned Vision
Transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with
35%/52% fewer parameters and 50%/62% fewer MACs. The effectiveness of
PoolFormer verifies our hypothesis and urges us to initiate the concept of
"MetaFormer", a general architecture abstracted from Transformers without
specifying the token mixer. Based on the extensive experiments, we argue that
MetaFormer is the key player in achieving superior results for recent
Transformer and MLP-like models on vision tasks. This work calls for more
future research dedicated to improving MetaFormer instead of focusing on the
token mixer modules. Additionally, our proposed PoolFormer could serve as a
starting baseline for future MetaFormer architecture design. Code is available
at this https URL

通过取代 Transformers 中的注意力机制，使用空间 MLPs，从而验证了 Transformers 的总体架构（MetaFormer）对模型性能的贡献更为关键。提出了 MetaFormer 的概念，这是一个抽象出 Transformers 中 token mixer 以外的通用架构，并将提出的 PoolFormer 作为未来 MetaFormer 架构设计的起点基线模型。