2701 results for "uber vip" across all locations
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Where and what to eat in Kolkata
The proof of the pudding is in the eating – sometimes, literally! Here are all the places Kolkata’s foodies have been heading to.
Around Bangalore in 24 hours
The top places to visit in an Uber in Bangalore in 24 hours. What to do, where to go, what to see and where to eat!
Top Destinations in Lagos – According to the Data
#UberPUPPIES in Minneapolis
Austin Marathon: Designated Pick-up and Drop-off
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
S. Duggal, S. Wang, W.-C. Ma, R. Hu, R. Urtasun
We propose a real-time dense depth estimation approach using stereo image pairs, which utilizes differentiable Patch Match to progressively prune the stereo matching search space. Our model achieves competitive performance on the KITTI benchmark despite running in real time. [PDF]
International Conference on Computer Vision (ICCV), 2019
DARNet: Deep Active Ray Network for Building Segmentation
D. Cheng, R. Liao, S. Fidler, R. Urtasun
In this paper, we propose a Deep Active Ray Network (DARNet) for automatic building segmentation. Taking an image as input, it first exploits a deep convolutional neural network (CNN) as the backbone to predict energy maps, which are further utilized to construct an energy function. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Bayesian inference on random simple graphs with power law degree distributions
J. Lee, C. Heaukulani, Z. Ghahramani, L. James, S. Choi
We present a model for random simple graphs with a degree distribution that obeys a power law (i.e., is heavy-tailed). To attain this behavior, the edge probabilities in the graph are constructed from Bertoin-Fujita-Roynette-Yor (BFRY) random variables, which have been recently utilized in Bayesian statistics for the construction of power law models in several applications. […] [PDF]
International Conference on Machine Learning (ICML), 2017
Towards Diverse and Natural Image Descriptions via a Conditional GAN
B. Dai, S. Fidler, R. Urtasun, D. Lin
In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712.5 km² of land, 8439 km of road and around 400,000 buildings. Our benchmark provides different perspectives of the world captured from airplanes, drones and cars driving around the city. […] [PDF]
International Conference on Computer Vision (ICCV), 2017