70 citations · 231 across the 35 of their papers we have counts for
16 papers · 1 filter
Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation
Rakshith Shetty, Bernt Schiele, Mario Fritz
Importance of visual context in scene understanding tasks is well recognized in the computer vision community. However, to what extent the computer vision models for image classifi…
Knockoff Nets: Stealing Functionality of Black-Box Models
Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz
Machine Learning (ML) models are increasingly deployed in the wild to perform a wide range of tasks. In this work, we ask to what extent can an adversary steal functionality of suc…
Meta-Transfer Learning for Few-Shot Learning
Qianru Sun, Yaoyao Liu, Tat-Seng Chua +1
Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order t…
Disentangling Adversarial Robustness and Generalization
David Stutz, Matthias Hein, Bernt Schiele
Obtaining deep networks that are robust against adversarial examples and generalize well is an open problem. A recent hypothesis even states that both robust and accurate models ar…
Parameter-Free Spatial Attention Network for Person Re-Identification
Haoran Wang, Yue Fan, Zexin Wang +2
Global average pooling (GAP) allows to localize discriminative information for recognition [40]. While GAP helps the convolution neural network to attend to the most discriminative…
Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods
Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
For autonomous agents to successfully operate in the real world, the ability to anticipate future scene states is a key competence. In real-world scenarios, future states become in…