1 citations · 1 across the 4 of their papers we have counts for
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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…
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…
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…
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…
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…