Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ConvMLP: Hierarchical Convolutional MLPs for Vision   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
https://github.com/SHI-Labs/Convolutional-MLPs.

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The categories of this paper are: hep-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: physics
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-ex