Papers › Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation
Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation
Hang Gao, Xizhou Zhu, Steve Lin, Jifeng Dai
Convolutional networks are not aware of an object's geometric variations, which leads to inefficient utilization of model and data capacity. To overcome this issue, recent works on deformation modeling seek to spatially reconfigure the data towards a common arrangement such that semantic recognition suffers less from deformation. This is typically done by augmenting static operators with learned free-form sampling grids in the image space, dynamically tuned to the data and task for adapting the receptive field. Yet adapting the receptive field does not quite reach the actual goal -- what really matters to the network is the "effective" receptive field (ERF), which reflects how much each pixel contributes. It is thus natural to design other approaches to adapt the ERF directly during runtime. In this work, we instantiate one possible solution as Deformable Kernels (DKs), a family of novel and generic convolutional operators for handling object deformations by directly adapting the ERF while leaving the receptive field untouched. At the heart of our method is the ability to resample the original kernel space towards recovering the deformation of objects. This approach is justified with theoretical insights that the ERF is strictly determined by data sampling locations and kernel values. We implement DKs as generic drop-in replacements of rigid kernels and conduct a series of empirical studies whose results conform with our theories. Over several tasks and standard base models, our approach compares favorably against prior works that adapt during runtime. In addition, further experiments suggest a working mechanism orthogonal and complementary to previous works.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | ResNet-50-DW (Deformable Kernels) | Top 1 Accuracy | 78.5% | #826 of 1060 | Archive leaderboard | report |
| Object Detection | COCO test-dev | ResNet-50-DW-DPN (Deformable Kernels) | APL | 53.3 | #191 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | ResNet-50-DW-DPN (Deformable Kernels) | APM | 43.9 | #191 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | ResNet-50-DW-DPN (Deformable Kernels) | APS | 24.6 | #191 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | ResNet-50-DW-DPN (Deformable Kernels) | box mAP | 40.6 | #191 of 225 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections