17 citations · 35 across the 10 of their papers we have counts for
21 papers
Evidential Deep Learning for Class-Incremental Semantic Segmentation
Karl Holmquist, Lena Klasén, Michael Felsberg
Class-Incremental Learning is a challenging problem in machine learning that aims to extend previously trained neural networks with new classes. This is especially useful if the sy…
Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer
Omkar Thawakar, Sanath Narayan, Jiale Cao +6
State-of-the-art transformer-based video instance segmentation (VIS) approaches typically utilize either single-scale spatio-temporal features or per-frame multi-scale features dur…
Visual Feature Encoding for GNNs on Road Networks
Oliver Stromann, Alireza Razavi, Michael Felsberg
In this work, we present a novel approach to learning an encoding of visual features into graph neural networks with the application on road network data. We propose an architectur…
A Bayesian Approach to Reinforcement Learning of Vision-Based Vehicular Control
Zahra Gharaee, Karl Holmquist, Linbo He +1
In this paper, we present a state-of-the-art reinforcement learning method for autonomous driving. Our approach employs temporal difference learning in a Bayesian framework to lear…
Deep Gaussian Processes for Few-Shot Segmentation
Joakim Johnander, Johan Edstedt, Martin Danelljan +2
Few-shot segmentation is a challenging task, requiring the extraction of a generalizable representation from only a few annotated samples, in order to segment novel query images. A…
Normalized Convolution Upsampling for Refined Optical Flow Estimation
Abdelrahman Eldesokey, Michael Felsberg
Optical flow is a regression task where convolutional neural networks (CNNs) have led to major breakthroughs. However, this comes at major computational demands due to the use of c…