Anisotropic convolution is a central building block of CNNs but challenging to transfer to surfaces. DeltaConv learns combinations and compositions of operators from vector calculus, which are a natural fit for curved surfaces. The result is a simple and robust anisotropic convolution operator for point clouds with state-of-the-art results.

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Spectral Dropout

Regularization of Deep Neural Networks with Spectral Dropout

DeltaConv

DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds

**Axial Attention** is a simple generalization of self-attention that naturally aligns with the multiple dimensions of the tensors in both the encoding and the decoding settings. It was first proposed in [CCNet](https://paperswithcode.com/method/ccnet) [1] named as criss-cross attention, which harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies. Ho et al [2] extents CCNet to process multi-dimensional data.  The proposed structure of the layers allows for the vast majority of the context to be computed in parallel during decoding without introducing any independence assumptions. It serves as the basic building block for developing self-attention-based autoregressive models for high-dimensional data tensors, e.g., Axial Transformers. It has been applied in [AlphaFold](https://paperswithcode.com/method/alphafold) [3] for interpreting protein sequences.

[1] Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, Wenyu Liu. CCNet: Criss-Cross Attention for Semantic Segmentation. ICCV, 2019.

[2] Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, Tim Salimans. arXiv:1912.12180

[3] Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A. Highly accurate protein structure prediction with AlphaFold. Nature. 2021 Jul 15:1-1.

Source	DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds
Year	2000
Data Source	Papers with Code archive (CC BY-SA)