MLP-based architectures, which consist of a sequence of consecutive
multi-layer perceptron blocks, have recently been found to reach comparable
results to convolutional and transformer-based methods. However, most adopt
spatial MLPs which take fixed dimension inputs, therefore making it difficult
to apply them to downstream tasks, such as object detection and semantic
segmentation. Moreover, single-stage designs further limit performance in other
computer vision tasks and fully connected layers bear heavy computation. To
tackle these problems, we propose ConvMLP: a hierarchical Convolutional MLP for
visual recognition, which is a light-weight, stage-wise, co-design of
convolution layers, and MLPs. In particular, ConvMLP-S achieves 76.8% top-1
accuracy on ImageNet-1k with 9M parameters and 2.4G MACs (15% and 19% of
MLP-Mixer-B/16, respectively). Experiments on object detection and semantic
segmentation further show that visual representation learned by ConvMLP can be
seamlessly transferred and achieve competitive results with fewer parameters.
Our code and pre-trained models are publicly available at
this https URL

提出了层次化的卷积 MLP，称其为 ConvMLP，可用于视觉识别、物体检测和语义分割等任务，与 MLP-Mixer-B/16 相比，ConvMLP-S 使用了更少的参数，且在 ImageNet-1k 数据集上具有较高的准确性。