Aug 22, 2024 · We propose a simple but highly effective method to address this problem, which utilizes a set of learnable biases called the G-Biases under the group order.

Aug 26, 2024 · The paper presents a compelling approach to improving the performance of computer vision models by relaxing the strict rotational equivariance constraint.

Aug 25, 2024 · Therefore, we focus on how to relax the strict G-transformation convolution fil- ter value-sharing problem to adapt to rotational Symmetry-.

This study introduces a new method called Relaxed Rotational Equivariant Convolution (RREConv) to better handle real-world data that doesn't follow strict ...

Group Equivariant Convolution (GConv) can effectively handle rotational symmetry data. They assume uniform and strict rotational symmetry across all ...

Aug 23, 2024 · They construct the Relaxed Rotational Equivariant Filter (RREF) by adding the G-Biases to the GConv filters, enabling the network to adapt to ...

Group Equivariant Convolution (GConv) can effectively handle rotational symmetry data. They assume uniform and strict rotational symmetry across all features, ...

Gradient-based learning of equivariance achieves similar or improved performance compared to the best value found by cross-validation and outperforms ...

Relaxed Rotational Equivariance via G-Biases in Vision. Preprint. Aug 2024 ... SBDet: A Symmetry-Breaking Object Detector via Relaxed Rotation-Equivariance.

Rotation equivariance is a desirable property in many practical applications such as motion forecasting and 3D perception, where it can offer benefits like ...