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Found 6 papers, 6 papers with code
Structured Initialization for Attention in Vision Transformers
By contrast, convolutional neural networks (CNNs) have an architectural inductive bias enabling them to perform well on such problems.
Convolutional Initialization for Data-Efficient Vision Transformers
Training vision transformer networks on small datasets poses challenges.
Robust Point Cloud Processing through Positional Embedding
End-to-end trained per-point embeddings are an essential ingredient of any state-of-the-art 3D point cloud processing such as detection or alignment.
Fast Neural Scene Flow
Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with dense lidar points.
Ranked #4 on
Self-supervised Scene Flow Estimation
on Argoverse 2
Trading Positional Complexity vs. Deepness in Coordinate Networks
It is well noted that coordinate-based MLPs benefit -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier features.
Rethinking Positional Encoding
It is well noted that coordinate based MLPs benefit -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier features.
