UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition
CVPR 2024(2023)
摘要
Large-kernel convolutional neural networks (ConvNets) have recently received
extensive research attention, but two unresolved and critical issues demand
further investigation. 1) The architectures of existing large-kernel ConvNets
largely follow the design principles of conventional ConvNets or transformers,
while the architectural design for large-kernel ConvNets remains
under-addressed. 2) As transformers have dominated multiple modalities, it
remains to be investigated whether ConvNets also have a strong universal
perception ability in domains beyond vision. In this paper, we contribute from
two aspects. 1) We propose four architectural guidelines for designing
large-kernel ConvNets, the core of which is to exploit the essential
characteristics of large kernels that distinguish them from small kernels -
they can see wide without going deep. Following such guidelines, our proposed
large-kernel ConvNet shows leading performance in image recognition (ImageNet
accuracy of 88.0
demonstrating better performance and higher speed than the recent powerful
competitors. 2) We discover large kernels are the key to unlocking the
exceptional performance of ConvNets in domains where they were originally not
proficient. With certain modality-related preprocessing approaches, the
proposed model achieves state-of-the-art performance on time-series forecasting
and audio recognition tasks even without modality-specific customization to the
architecture. All the code and models are publicly available on GitHub and
Huggingface.
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