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Jia, D., Wei, D., Socher R., Li, L.-J., Li, K. and Li, F-F. (2009) Imagenet: A Large-Scale Hierarchical Image Database. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, 20-25 June 2009, 248-255.

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• TITLE: Dual Channel with Involution for Long-Tailed Visual Recognition

  AUTHORS: Mengxue Li

  KEYWORDS: Long-Tailed Recognition, Deep Neural Network, Dual-Channel Structure, Involution

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.12 No.4, April 7, 2022

  ABSTRACT: With the rapid increase of large-scale problems, the distribution of real-world datasets tends to be long-tailed. Existing solutions typically involve re-balancing strategies (i.e., re-sampling and re-weighting). Although they can significantly promote the classifier learning of deep networks, they will unexpectedly impair the representative ability of the learned deep features to a certain extent. Therefore, this paper proposes a dual-channel learning algorithm with involution neural networks (DC-Invo) to take care of representation learning and classifier learning concurrently. In this work, the most important thing is to combine ResNet and involution to obtain higher classification accuracy because of involution’s wider coverage in the spatial dimension. The paper conducted extensive experiments on several benchmark vision tasks including Cifar-LT, Imagenet-LT, and Places-LT, showing that DC-Invo is able to achieve significant performance gained on long-tailed datasets.