KAConvNet introduces a Kolmogorov-Arnold Convolutional Layer to build networks competitive with ViTs and CNNs while offering stronger theoretical interpretability.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3representative citing papers
DDF2Pol fuses real and complex domain features with attention to reach 98.16% OA on Flevoland and 96.12% on San Francisco PolSAR datasets using only 91k parameters.
citing papers explorer
-
KAConvNet: Kolmogorov-Arnold Convolutional Networks for Vision Recognition
KAConvNet introduces a Kolmogorov-Arnold Convolutional Layer to build networks competitive with ViTs and CNNs while offering stronger theoretical interpretability.
-
DDF2Pol: A Dual-Domain Feature Fusion Network for PolSAR Image Classification
DDF2Pol fuses real and complex domain features with attention to reach 98.16% OA on Flevoland and 96.12% on San Francisco PolSAR datasets using only 91k parameters.
- Bidirectional Cross-Attention Fusion of High-Resolution RGB and Low-Resolution Hyperspectral Inputs for Multimodal Semantic Segmentation