3DV 2017 We propose using a generative adversarial network (GAN) to assist a novice user in designing real-world shapes with a simple interface.
CVPR 2017 We show that dilated residual networks (DRNs) outperform their non-dilated counterparts in image classification without increasing the model’s depth or complexity.
CVPR 2017 Oral We develop an end-to-end trainable architecture for learning to predict a distribution over future vehicle egomotion from instantaneous monocular camera observations and previous vehicle state.
CVPR 2017 We propose a deep adversarial image synthesis architecture that is conditioned on sketched boundaries and sparse color strokes to generate realistic images.
CVPR 2017 Oral Our network uses a dilation-based 3D context module to efficiently expand the receptive field and enable 3D context learning.
3DOR 2017 This track provides a benchmark to evaluate large-scale 3D shape retrieval based on the ShapeNet dataset.
arXiv 2016 We introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems.
Siggraph 2016 We seek a relative quality measure within a series of photos taken of the same scene, which can be used for automatic photo triage.
ICLR 2016 We study dilated convolution in depth. It has become a foundamental network operation.