20,864 research outputs found
Shadow-Aware Dynamic Convolution for Shadow Removal
With a wide range of shadows in many collected images, shadow removal has
aroused increasing attention since uncontaminated images are of vital
importance for many downstream multimedia tasks. Current methods consider the
same convolution operations for both shadow and non-shadow regions while
ignoring the large gap between the color mappings for the shadow region and the
non-shadow region, leading to poor quality of reconstructed images and a heavy
computation burden. To solve this problem, this paper introduces a novel
plug-and-play Shadow-Aware Dynamic Convolution (SADC) module to decouple the
interdependence between the shadow region and the non-shadow region. Inspired
by the fact that the color mapping of the non-shadow region is easier to learn,
our SADC processes the non-shadow region with a lightweight convolution module
in a computationally cheap manner and recovers the shadow region with a more
complicated convolution module to ensure the quality of image reconstruction.
Given that the non-shadow region often contains more background color
information, we further develop a novel intra-convolution distillation loss to
strengthen the information flow from the non-shadow region to the shadow
region. Extensive experiments on the ISTD and SRD datasets show our method
achieves better performance in shadow removal over many state-of-the-arts. Our
code is available at https://github.com/xuyimin0926/SADC
Deformable Convolutional Networks
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
- …