25 citations · 78 across the 25 of their papers we have counts for
5 papers · 1 filter
A Parametric Top-View Representation of Complex Road Scenes
Ziyan Wang, Buyu Liu, Samuel Schulter +1
In this paper, we address the problem of inferring the layout of complex road scenes given a single camera as input. To achieve that, we first propose a novel parameterized model o…
Learning To Simulate
Nataniel Ruiz, Samuel Schulter, Manmohan Chandraker
Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire. In this work, we propose a reinforcement le…
Learning to Look around Objects for Top-View Representations of Outdoor Scenes
Samuel Schulter, Menghua Zhai, Nathan Jacobs +1
Given a single RGB image of a complex outdoor road scene in the perspective view, we address the novel problem of estimating an occlusion-reasoned semantic scene layout in the top-…
Memory Warps for Learning Long-Term Online Video Representations
Tuan-Hung Vu, Wongun Choi, Samuel Schulter +1
This paper proposes a novel memory-based online video representation that is efficient, accurate and predictive. This is in contrast to prior works that often rely on computational…
Learning to Adapt Structured Output Space for Semantic Segmentation
Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter +3
Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. As t…