Deformable Convolutional Network: A Complete Guide | Medium
2024年2月1日 · It is clear how the deformable convolution can encapsulate objects of different shapes, allowing for more accurate and reliable feature extraction. A key issue with traditional convolutional...
[1703.06211] Deformable Convolutional Networks - arXiv.org
2017年3月17日 · 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.
OpenGVLab/DCNv4: [CVPR 2024] Deformable Convolution v4 - GitHub
We introduce Deformable Convolution v4 (DCNv4), a highly efficient and effective operator designed for a broad spectrum of vision applications. DCNv4 addresses the limitations of its predecessor, DCNv3, with two key enhancements: 1. removing softmax normalization in spatial aggregation to enhance its dynamic property and expressive power and 2 ...
Type of convolutions: Deformable and Transformable Convolution
2019年7月7日 · Deformable convolution makes the sampling matrix learn-able, allowing the shape of the kernel to adopt to the unknown complex transformations in the input. Let’s take a look at the following figure to understand the concept.
In this work, we introduce two new modules to enhance the transformation modeling capability of CNNs, namely, de-formable convolution and deformable RoI pooling.
Deformable Convolutional Networks - IEEE Xplore
2017年12月25日 · 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 performance of our approach.
Deformable ConvNets v2: More Deformable, Better Results
2018年11月27日 · To address this problem, we present a reformulation of Deformable ConvNets that improves its ability to focus on pertinent image regions, through increased modeling power and stronger training.
msracver/Deformable-ConvNets: Deformable Convolutional Networks - GitHub
2019年4月15日 · We provide trained deformable convnet models, including the deformable R-FCN & Faster R-CNN models trained on COCO trainval, and the deformable DeepLab model trained on CityScapes train. To use the demo with our pre-trained deformable models, please download manually from OneDrive or BaiduYun , and put it under folder model/ .
Deformable Convolution Explained - Papers With Code
2019年1月6日 · Deformable convolutions add 2D offsets to the regular grid sampling locations in the standard convolution. It enables free form deformation of the sampling grid. The offsets are learned from the preceding feature maps, via additional convolutional layers.
To address this problem, we present a reformulation of De-formable ConvNets that improves its ability to focus on per-tinent image regions, through increased modeling power and stronger training.