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Deformable Kernel

8 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

A Deformable Kernels is a type of convolutional operator for deformation modeling. DKs learn free-form offsets on kernel coordinates to deform the original kernel space towards specific data modality, rather than recomposing data. This can directly adapt the effective receptive field (ERF) while leaving the receptive field untouched. They can be used as a drop-in replacement of rigid kernels.

As shown in the Figure, for each input patch, a local DK first generates a group of kernel offsets {Δ𝓀} from input feature patch using the light-weight generator 𝒢 (a 3×3 convolution of rigid kernel). Given the original kernel weights 𝒲 and the offset group {Δ𝓀}, DK samples a new set of kernel 𝒲′ using a bilinear sampler ℬ. Finally, DK convolves the input feature map and the sampled kernels to complete the whole computation.

Source: Deformable Kernels: Adapting Effective Receptive Fields...See Code · hangg7/deformable-kernels

Papers archive 2025-07-28

8 shown of 8, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

18 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Object2
Object Detection2
3D Object Detection1
Depth Completion1
Depth Map Super-Resolution1
Graph Matching1
Image Classification1
Image Restoration1
Object Recognition1
Robust Object Detection1
Scene Text Detection1
Segmentation1
Semantic Segmentation1
Super-Resolution1
Text Detection1
Video Frame Interpolation1
Video Super-Resolution1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with Deformable Kernel: 2019 to 2025, peak 2 2 0 2019: 2 papers 2019 2020: 1 paper 2020 2021: 0 papers 2021 2022: 2 papers 2022 2023: 2 papers 2023 2024: 0 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (8 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Convolutions

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