72 citations · 98 across the 6 of their papers we have counts for
6 papers · 1 filter
AttEntropy: On the Generalization Ability of Supervised Semantic Segmentation Transformers to New Objects in New Domains
Krzysztof Lis, Matthias Rottmann, Annika Mütze +3
In addition to impressive performance, vision transformers have demonstrated remarkable abilities to encode information they were not trained to extract. For example, this informat…
Perspective Aware Road Obstacle Detection
Krzysztof Lis, Sina Honari, Pascal Fua +1
While road obstacle detection techniques have become increasingly effective, they typically ignore the fact that, in practice, the apparent size of the obstacles decreases as their…
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
Robin Chan, Krzysztof Lis, Svenja Uhlemeyer +6
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle pre…
Detecting Road Obstacles by Erasing Them
Krzysztof Lis, Sina Honari, Pascal Fua +1
Vehicles can encounter a myriad of obstacles on the road, and it is impossible to record them all beforehand to train a detector. Instead, we select image patches and inpaint them…
Detecting the Unexpected via Image Resynthesis
Krzysztof Lis, Krishna Nakka, Pascal Fua +1
Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training. In this paper, we ta…
Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation
Weizhe Liu, Krzysztof Lis, Mathieu Salzmann +1
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density in the image plane. While useful for this purpose, this image-plane d…