title: Frequency Dynamic Convolution for Dense Image Prediction
summary: While Dynamic Convolution (DY-Conv) has shown promising performance by
  enabling adaptive weight selection through multiple parallel weights combined
  with an attention mechanism, the frequency response of these weights tends to
  exhibit high similarity, resulting in high parameter costs but limited
  adaptability. In this work, we introduce Frequency Dynamic Convolution
  (FDConv), a novel approach that mitigates these limitations by learning a fixed
  parameter budget in the Fourier domain. FDConv divides this budget into
  frequency-based groups with disjoint Fourier indices, enabling the construction
  of frequency-diverse weights without increasing the parameter cost. To further
  enhance adaptability, we propose Kernel Spatial Modulation (KSM) and Frequency
  Band Modulation (FBM). KSM dynamically adjusts the frequency response of each
  filter at the spatial level, while FBM decomposes weights into distinct
  frequency bands in the frequency domain and modulates them dynamically based on
  local content. Extensive experiments on object detection, segmentation, and
  classification validate the effectiveness of FDConv. We demonstrate that when
  applied to ResNet-50, FDConv achieves superior performance with a modest
  increase of +3.6M parameters, outperforming previous methods that require
  substantial increases in parameter budgets (e.g., CondConv +90M, KW +76.5M).
  Moreover, FDConv seamlessly integrates into a variety of architectures,
  including ConvNeXt, Swin-Transformer, offering a flexible and efficient
  solution for modern vision tasks. The code is made publicly available at
  https://github.com/Linwei-Chen/FDConv.
paper_authors._id: 67e2a43d5116df47da357eec
paper_authors.name: Linwei Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T14:36:22.430Z
paper_authors._id: 642438eaa3adbc7142c3ca0f
paper_authors.avatarUrl: /avatars/8deff70e0c93d259a42ee47f00a31e3e.svg
paper_authors.fullname: CharlesChen
paper_authors.type: user
paper_authors.user: CharlesChen2023
paper_authors._id: 67e2a43d5116df47da357eed
paper_authors.name: Lin Gu
paper_authors._id: 67e2a43d5116df47da357eee
paper_authors.name: Liang Li
paper_authors._id: 67e2a43d5116df47da357eef
paper_authors.name: Chenggang Yan
paper_authors._id: 67e2a43d5116df47da357ef0
paper_authors.name: Ying Fu
paper_title: Frequency Dynamic Convolution for Dense Image Prediction
paper_summary: While Dynamic Convolution (DY-Conv) has shown promising performance by
  enabling adaptive weight selection through multiple parallel weights combined
  with an attention mechanism, the frequency response of these weights tends to
  exhibit high similarity, resulting in high parameter costs but limited
  adaptability. In this work, we introduce Frequency Dynamic Convolution
  (FDConv), a novel approach that mitigates these limitations by learning a fixed
  parameter budget in the Fourier domain. FDConv divides this budget into
  frequency-based groups with disjoint Fourier indices, enabling the construction
  of frequency-diverse weights without increasing the parameter cost. To further
  enhance adaptability, we propose Kernel Spatial Modulation (KSM) and Frequency
  Band Modulation (FBM). KSM dynamically adjusts the frequency response of each
  filter at the spatial level, while FBM decomposes weights into distinct
  frequency bands in the frequency domain and modulates them dynamically based on
  local content. Extensive experiments on object detection, segmentation, and
  classification validate the effectiveness of FDConv. We demonstrate that when
  applied to ResNet-50, FDConv achieves superior performance with a modest
  increase of +3.6M parameters, outperforming previous methods that require
  substantial increases in parameter budgets (e.g., CondConv +90M, KW +76.5M).
  Moreover, FDConv seamlessly integrates into a variety of architectures,
  including ConvNeXt, Swin-Transformer, offering a flexible and efficient
  solution for modern vision tasks. The code is made publicly available at
  https://github.com/Linwei-Chen/FDConv.
paper_ai_summary: FDConv improves performance in modern vision tasks by learning frequency-diverse weights with a fixed parameter budget and integrating modulation techniques in both spatial and frequency domains.
paper_ai_keywords: Dynamic Convolution DY-Conv Frequency Dynamic Convolution FDConv Fourier domain frequency-based groups Kernel Spatial Modulation KSM Frequency Band Modulation FBM object detection segmentation classification ResNet-50 ConvNeXt Swin-Transformer