Dynamic Convolution

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MCL: Meta Convolution Layer
Oct 8, 2026Naim Reza, Md Al Amin, Ho Yub JungDynamic Convolution Convolutional Neural Networks
Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea
ENCORE: Efficient Noise Context-Aware Representation for Low-Dose CT Denoising
Aug 11, 2026Minwoo Yu, N. Robert Bennett, Jongduk Baek +1Dynamic Convolution Computed Tomography
Department of Artificial Intelligence, Yonsei University, South Korea · Department of Radiology, Stanford University, Stanford, CA 94305 USA · Department of Radiology and the Department of Electrical Engineering, Stanford University, Stanford, CA 94305 USA
Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution
Jul 21, 2026Zhenglong Liu, Wangyou Zhang, Chenda Li +1Dynamic Convolution Speech Enhancement
Auditory Cognition and Computational Acoustics Lab Shanghai Jiao Tong University, Shanghai, China · VUI Labs
Seeing Through WiFi: Lightweight Human Pose Estimation with Dynamic Kernel Attention
Jul 3, 2026Toan D. Gian, Van-Dinh Nguyen, Vo Phi Son +5Dynamic Convolution Channel Attention
Smart Green Transformation Center (GREEN-X), VinUniversity, Vietnam · Trinity College Dublin, Ireland · University of Oulu, Finland +3
CNN Models for Microphone Array Covariance Matrix Upsampling and Acoustic Imaging
Jul 1, 2026Marianthi Adamopoulou, Parthasaarathy Sudarsanam, David Diaz-Guerra +5Dynamic Convolution Convolutional Neural Networks
Dept. of Comp. and Elec. Engineering Mid Sweden University Sundsvall, Sweden · Audio Research Group, Tampere University Tampere, Finland
Dynamic Short Convolutions Improve Transformers
Jun 2, 2026Oliver Sieberling, Bharat Runwal, Rameswar Panda +1Dynamic Convolution Transformer
Massachusetts Institute of Technology · MIT-IBM Watson AI Lab
Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Network
May 16, 2026Baogui Huan, Chuanzheng Gong, Dezhong Chen +3Dynamic Convolution Data Augmentation
State Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao 266100, China · Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762 USA
RAFNet: Region-Aware Fusion Network for Pansharpening
May 4, 2026Jianing Zhang, Zijian Zhou, Kai SunDynamic Convolution Frequency-Domain Feature Learning
College of Artificial Intelligence, Xi’an Jiaotong University, Xi’an 710049, China · School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China
EAPFusion: Intrinsic Evolving Auxiliary Prior Guidance for Infrared and Visible Image Fusion
May 3, 2026Zhenyu Sun, Luobin Zhang, Axi Niu +2Dynamic Convolution Multi-Source Image Fusion
Northwestern Polytechnical University, Xi’an, China
Dynamic High-frequency Convolution for Infrared Small Target Detection
Feb 3, 2026Ruojing Li, Chao Xiao, Qian Yin +5Dynamic Convolution Frequency-Domain Feature Learning
The authors are with the College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China.
Conditional Clifford-Steerable CNNs for PDE Modeling
Oct 15, 2025Bálint László Szarvas, Maksim ZhdanovDynamic Convolution PDE Surrogate Modeling
University of Amsterdam · 2AMLab.

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MCL: Meta Convolution Layer

Naim Reza, Md Al Amin, Ho Yub Jung

Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea

Dynamic Convolution Convolutional Neural Networks Image Classification

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Dynamic convolution enhances convolutional neural networks (CNNs) by adapting kernels to input content, but it expresses the effective kernel as a linear mixture of a small number of basis kernels, which limits expressivity and complicates optimization as the mixture size grows. In this work, we revisit dynamic convolution from a functional perspective and propose the Meta Convolution Layer (MCL), which directly models the convolutional kernel as an input-conditioned function W(x) realized via a high-order polynomial expansion. Leveraging nested residual blocks inspired by deep polynomial networks, MCL implements a structured polynomial meta-network that generates a single input-adaptive kernel, thereby decoupling representational power from the explicit number of mixture kernels and alleviating training instability. MCL is a plug-in addition with standard convolutions and can be seamlessly integrated into both CNN and transformer backbones. Experimental evaluation shows that adding MCL improves the Top-1 accuracy of Resnet- 18, Resnet-50 and ResNet-101 by 6.61%, 3.42% and 3.05% on the ImageNet dataset. Moreover, the proposed method significantly boosts the accuracy of Resnet and Wide-Resnet variants on CIFAR-10 and CIFAR-100 datasets. Additionally, the proposed method outperforms previous methods on fine-grained visual classification tasks using Swin and ViT backbones. These results demonstrate that high-order polynomial kernel generation is a powerful and scalable alternative to linear mixture based dynamic convolution.

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