1.4k citations · 2.6k across the 155 of their papers we have counts for
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Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers
Nikola Popovic, Danda Pani Paudel, Thomas Probst +1
We introduce token-consistent stochastic layers in vision transformers, without causing any severe drop in performance. The added stochasticity improves network calibration, robust…
End-to-End Learning of Multi-category 3D Pose and Shape Estimation
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
In this paper, we study the representation of the shape and pose of objects using their keypoints. Therefore, we propose an end-to-end method that simultaneously detects 2D keypoin…
Topology Preserving Local Road Network Estimation from Single Onboard Camera Image
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
Knowledge of the road network topology is crucial for autonomous planning and navigation. Yet, recovering such topology from a single image has only been explored in part. Furtherm…
Efficient Visual Tracking with Exemplar Transformers
Philippe Blatter, Menelaos Kanakis, Martin Danelljan +1
The design of more complex and powerful neural network models has significantly advanced the state-of-the-art in visual object tracking. These advances can be attributed to deeper…
Implicit Neural Representations for Image Compression
Yannick Strümpler, Janis Postels, Ren Yang +2
Recently Implicit Neural Representations (INRs) gained attention as a novel and effective representation for various data types. Thus far, prior work mostly focused on optimizing t…
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
Lukas Hoyer, Dengxin Dai, Luc Van Gool
As acquiring pixel-wise annotations of real-world images for semantic segmentation is a costly process, a model can instead be trained with more accessible synthetic data and adapt…