RelFlexformers enable flexible integrable 3D RPE in attention via NU-FFT, generalizing prior methods to heterogeneous token positions with O(L log L) complexity.
arXiv preprint arXiv:2403.06467 (2024)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
MT-PCR is a hybrid Mamba-Transformer model for point cloud registration that uses Z-order spatial serialization to improve efficiency and accuracy over Transformer-only approaches.
Presents an SSM-based hierarchical feature learning method for medical point clouds that reports superior performance on classification, completion, and segmentation using a new dataset MedPointS.
citing papers explorer
-
RelFlexformer: Efficient Attention 3D-Transformers for Integrable Relative Positional Encodings
RelFlexformers enable flexible integrable 3D RPE in attention via NU-FFT, generalizing prior methods to heterogeneous token positions with O(L log L) complexity.
-
MT-PCR: Hybrid Mamba-Transformer Network with Spatial Serialization for Point Cloud Registration
MT-PCR is a hybrid Mamba-Transformer model for point cloud registration that uses Z-order spatial serialization to improve efficiency and accuracy over Transformer-only approaches.
-
Hierarchical Feature Learning for Medical Point Clouds via State Space Model
Presents an SSM-based hierarchical feature learning method for medical point clouds that reports superior performance on classification, completion, and segmentation using a new dataset MedPointS.