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Found 10 papers, 3 papers with code
SPIGAN: Privileged Adversarial Learning from Simulation
Deep Learning for Computer Vision depends mainly on the source of supervision. Photo-realistic simulators can generate large-scale automatically labeled syntheticdata, but introduce a domain gap negatively impacting performance.
Joint Coarse-And-Fine Reasoning for Deep Optical Flow
We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning.
Physical Representation-based Predicate Optimization for a Visual Analytics Database
Modern extraction techniques are based on deep convolutional neural networks (CNNs) and can classify objects within images with astounding accuracy.
A Dataset To Evaluate The Representations Learned By Video Prediction Models
We present a parameterized synthetic dataset called Moving Symbols to support the objective study of video prediction networks.
CARLA: An Open Urban Driving Simulator
We introduce CARLA, an open-source simulator for autonomous driving research.
From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example
However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA).
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes
In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations.
Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation
In this work we investigate the problem of road scene semantic segmentation using Deconvolutional Networks (DNs).
Fast and Robust Fixed-Rank Matrix Recovery
We address the problem of efficient sparse fixed-rank (S-FR) matrix decomposition, i. e., splitting a corrupted matrix $M$ into an uncorrupted matrix $L$ of rank $r$ and a sparse matrix of outliers $S$.
Motion Estimation via Robust Decomposition with Constrained Rank
To this end we propose the Robust Decomposition with Constrained Rank (RD-CR), a proximal gradient based method that enforces the rank constraints inherent to motion estimation.
