CNN(I_k) = f_{cn}(\text{convolution}(I_k)) \quad \text{(3)} \\理论上,卷积神经网络在小范围内享有旋转等变性。如(2)所示, P_k 不会因旋转而有很大变化。在旋转的情况下,如果 f_{cn} 被适当训练, P_k 将映射到相同的标签 l_k 。 卷积神经网络集合学习算法 假设图像的最大旋转角度是θ,...
These features-initially just the basic element type of each atom-are updated through a series of neural network layers featuring rotation-equivariant convolutions. Starting from all atoms, we further aggregate information at the level of alpha carbons before making a prediction at the level of the...
In this paper, we propose three 3D CNN architectures that are both globally equivariant and locally invariant to rotations (see Fig. 1 for an illustration in 2D), and can combine this with directionally sensitive image analysis. This can be achieved by convolving with rotated filters (i.e. ...
Full-model group equivariance is achieved by replacing all convolution layers with group-equivariant versions [5]. Batch normalization layers [13] are made group-equivariant by aggregating moments pergroupfeature map rather than spatial feature map (as proposed by [5]). Zero-padding is removed to ...
CNNs on Surfaces using Rotation-Equivariant Features Update 2022If you are looking into using HSN for your own learning task, we would recommend giving DeltaConv a look. It works on point clouds as well as meshes (simply use the vertex positions) and is a lot more efficient in time and ...
Spezialetti R, Salti S, Di Stefano L (2019) Learning an effective equivariant 3D descriptor without supervision. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp 6401–6410). https://doi.org/10.1109/ICCV.2019.00650 Charles RQ, Su H, Kaichun M, Guibas LJ (2017...
Motivated by the fact that in the early layers of CNNs distinct filters often encode for the same feature at different angles, we propose to incorporate the rotation equivariant prior in these models. In this work, different regularization strategies that capture the notion of approximate ...
To achieve more group equivariances, rotation group equivariant convolutions(RGEC) are proposed to acquire both translation and rotation group equivariances.However, previous work paid more attention to the number of parameters and usually ignored other resource costs. In this paper, we construct our...
We present the R otation E quivariant CO nvolutional Neural Network (RECO), a model specifically designed for pedestrian trajectory prediction using rotation equivariant convolutions. We evaluate our model on challenging real-world human trajectory forecasting datasets and show that it achieves ...
To achieve more group equivariances, rotation group equivariant convolutions (RGEC) are proposed to acquire both translation and rotation group equivariances. However, previous work paid more attention to the number of parameters and usually ignored other resource costs. In this paper, we construct ...