KAConvNet introduces a Kolmogorov-Arnold Convolutional Layer to build networks competitive with ViTs and CNNs while offering stronger theoretical interpretability.
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3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
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cs.CV 3years
2026 3representative citing papers
BCAF fuses native-grid high-res RGB and low-res HSI via bidirectional cross-attention in adapted Swin Transformers to reach state-of-the-art mIoU on SpectralWaste and a new industrial dataset while running at real-time speeds.
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
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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.
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Bidirectional Cross-Attention Fusion of High-Res RGB and Low-Res HSI for Multimodal Automated Waste Sorting
BCAF fuses native-grid high-res RGB and low-res HSI via bidirectional cross-attention in adapted Swin Transformers to reach state-of-the-art mIoU on SpectralWaste and a new industrial dataset while running at real-time speeds.
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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.