Deformable Convolutional Networks
read the original abstract
Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we introduce two new modules to enhance the transformation modeling capacity of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the effectiveness of our approach on sophisticated vision tasks of object detection and semantic segmentation. The code would be released.
This paper has not been read by Pith yet.
Forward citations
Cited by 4 Pith papers
-
Mapped Convolutions
Mapped convolutions generalize standard convolutions by decoupling sampling and weighting, enabling direct convolution on spherical and mesh data with a 17% improvement in spherical depth estimation.
-
UniFixer: A Universal Reference-Guided Fixer for Diffusion-Based View Synthesis
UniFixer is a universal reference-guided framework that fixes spatial, temporal, and backbone-related degradations in diffusion-based view synthesis via coarse-to-fine modules and achieves zero-shot SOTA results on no...
-
Reprojection R-CNN: A Fast and Accurate Object Detector for 360{\deg} Images
Reprojection R-CNN is a two-stage detector for 360° images combining a distortion-aware spherical RPN on ERP with a reprojection network on perspective projections, reporting higher mAP than prior methods on two new s...
-
Rethinking Atrous Convolution for Semantic Image Segmentation
DeepLabv3 improves semantic segmentation by capturing multi-scale context with cascaded or parallel atrous convolutions and adding global context to ASPP, achieving better results on PASCAL VOC 2012 without DenseCRF p...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.