Publications (18)
A Simulator Dataset to Support the Study of Impaired Driving
John Gideon, Kimimasa Tamura, Emily Sumner +7
Despite recent advances in automated driving technology, impaired driving continues to incur a high cost to society. In this paper, we present a driving dataset designed to support…
A Dataset To Evaluate The Representations Learned By Video Prediction Models
Ryan Szeto, Simon Stent, German Ros +1
We present a parameterized synthetic dataset called Moving Symbols to support the objective study of video prediction networks. Using several instantiations of the dataset in which…
Shadows Shed Light on 3D Objects
Ruoshi Liu, Sachit Menon, Chengzhi Mao +3
3D reconstruction is a fundamental problem in computer vision, and the task is especially challenging when the object to reconstruct is partially or fully occluded. We introduce a…
MAAD: A Model and Dataset for "Attended Awareness" in Driving
Deepak Gopinath, Guy Rosman, Simon Stent +4
We propose a computational model to estimate a person's attended awareness of their environment. We define attended awareness to be those parts of a potentially dynamic scene which…
CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization
Eesha Kumar, Yiming Zhang, Stefano Pini +4
The imitation learning of self-driving vehicle policies through behavioral cloning is often carried out in an open-loop fashion, ignoring the effect of actions to future states. Tr…
Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications
Lingzhi Zhang, Shenghao Zhou, Simon Stent +1
Egocentric videos offer fine-grained information for high-fidelity modeling of human behaviors. Hands and interacting objects are one crucial aspect of understanding a viewer's beh…
SceneNet: Understanding Real World Indoor Scenes With Synthetic Data
Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan +2
Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments. Recent attempts with supervised learning…
Learning Latent Traits for Simulated Cooperative Driving Tasks
Jonathan A. DeCastro, Deepak Gopinath, Guy Rosman +3
To construct effective teaming strategies between humans and AI systems in complex, risky situations requires an understanding of individual preferences and behaviors of humans. Pr…
Tracking through Containers and Occluders in the Wild
Basile Van Hoorick, Pavel Tokmakov, Simon Stent +2
Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems. In this paper, we introduce , a ne…
LocTex: Learning Data-Efficient Visual Representations from Localized Textual Supervision
Zhijian Liu, Simon Stent, Jie Li +2
Computer vision tasks such as object detection and semantic/instance segmentation rely on the painstaking annotation of large training datasets. In this paper, we propose LocTex th…
Gaze360: Physically Unconstrained Gaze Estimation in the Wild
Petr Kellnhofer, Adria Recasens, Simon Stent +2
Understanding where people are looking is an informative social cue. In this work, we present Gaze360, a large-scale gaze-tracking dataset and method for robust 3D gaze estimation…
The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Video
John Gideon, Simon Stent
The ability to reliably estimate physiological signals from video is a powerful tool in low-cost, pre-clinical health monitoring. In this work we propose a new approach to remote p…
Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation
German Ros, Simon Stent, Pablo F. Alcantarilla +1
In this work we investigate the problem of road scene semantic segmentation using Deconvolutional Networks (DNs). Several constraints limit the practical performance of DNs in this…
Revealing Occlusions with 4D Neural Fields
Basile Van Hoorick, Purva Tendulkar, Didac Suris +3
For computer vision systems to operate in dynamic situations, they need to be able to represent and reason about object permanence. We introduce a framework for learning to estimat…
SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes
Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan +2
We are interested in automatic scene understanding from geometric cues. To this end, we aim to bring semantic segmentation in the loop of real-time reconstruction. Our semantic seg…
Seeing Faces in Things: A Model and Dataset for Pareidolia
Mark Hamilton, Simon Stent, Vasha DuTell +4
The human visual system is well-tuned to detect faces of all shapes and sizes. While this brings obvious survival advantages, such as a better chance of spotting unknown predators…
Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks
Adrià Recasens, Petr Kellnhofer, Simon Stent +2
We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task. Our differentiable layer…
gvnn: Neural Network Library for Geometric Computer Vision
Ankur Handa, Michael Bloesch, Viorica Patraucean +3
We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning. Inspired by the recent success of S…