4 citations · 5 across the 5 of their papers we have counts for
5 papers
Differentially Private Heatmaps
Badih Ghazi, Junfeng He, Kai Kohlhoff +4
We consider the task of producing heatmaps from users' aggregated data while protecting their privacy. We give a differentially private (DP) algorithm for this task and demonstrate…
Deep Saliency Prior for Reducing Visual Distraction
Kfir Aberman, Junfeng He, Yossi Gandelsman +5
Using only a model that was trained to predict where people look at images, and no additional training data, we can produce a range of powerful editing effects for reducing distrac…
Semi-synthesis: A fast way to produce effective datasets for stereo matching
Ju He, Enyu Zhou, Liusheng Sun +3
Stereo matching is an important problem in computer vision which has drawn tremendous research attention for decades. Recent years, data-driven methods with convolutional neural ne…
Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion
Adam Kortylewski, Ju He, Qing Liu +1
Recent findings show that deep convolutional neural networks (DCNNs) do not generalize well under partial occlusion. Inspired by the success of compositional models at classifying…
Exploring Hypergraph Representation on Face Anti-spoofing Beyond 2D Attacks
Wei Hu, Gusi Te, Ju He +2
Face anti-spoofing plays a crucial role in protecting face recognition systems from various attacks. Previous model-based and deep learning approaches achieve satisfactory performa…