24 citations · 52 across the 13 of their papers we have counts for
12 papers · 1 filter
Differentiable Voronoi Ray Tracing Beyond Rasterization Speeds
Bernardo Taveira, Carl Lindström, Joakim Johnander +1
Real-time novel view synthesis is dominated by rasterized explicit primitives. These projection-based pipelines provide high throughput but require specialized extensions for non-p…
Predicting Signed Distance Functions for Visual Instance Segmentation
Emil Brissman, Joakim Johnander, Michael Felsberg
Visual instance segmentation is a challenging problem and becomes even more difficult if objects of interest varies unconstrained in shape. Some objects are well described by a rec…
NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving
William Ljungbergh, Adam Tonderski, Joakim Johnander +4
We present a versatile NeRF-based simulator for testing autonomous driving (AD) software systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation…
You can have your ensemble and run it too -- Deep Ensembles Spread Over Time
Isak Meding, Alexander Bodin, Adam Tonderski +3
Ensembles of independently trained deep neural networks yield uncertainty estimates that rival Bayesian networks in performance. They also offer sizable improvements in terms of pr…
Raw or Cooked? Object Detection on RAW Images
William Ljungbergh, Joakim Johnander, Christoffer Petersson +1
Images fed to a deep neural network have in general undergone several handcrafted image signal processing (ISP) operations, all of which have been optimized to produce visually ple…
Future Object Detection with Spatiotemporal Transformers
Adam Tonderski, Joakim Johnander, Christoffer Petersson +1
We propose the task Future Object Detection, in which the goal is to predict the bounding boxes for all visible objects in a future video frame. While this task involves recognizin…
Dense Gaussian Processes for Few-Shot Segmentation
Joakim Johnander, Johan Edstedt, Michael Felsberg +2
Few-shot segmentation is a challenging dense prediction task, which entails segmenting a novel query image given only a small annotated support set. The key problem is thus to desi…
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…
Learning Video Instance Segmentation with Recurrent Graph Neural Networks
Joakim Johnander, Emil Brissman, Martin Danelljan +1
Most existing approaches to video instance segmentation comprise multiple modules that are heuristically combined to produce the final output. Formulating a purely learning-based m…
A Generative Appearance Model for End-to-end Video Object Segmentation
Joakim Johnander, Martin Danelljan, Emil Brissman +2
One of the fundamental challenges in video object segmentation is to find an effective representation of the target and background appearance. The best performing approaches resort…
Unveiling the Power of Deep Tracking
Goutam Bhat, Joakim Johnander, Martin Danelljan +2
In the field of generic object tracking numerous attempts have been made to exploit deep features. Despite all expectations, deep trackers are yet to reach an outstanding level of…
DCCO: Towards Deformable Continuous Convolution Operators
Joakim Johnander, Martin Danelljan, Fahad Shahbaz Khan +1
Discriminative Correlation Filter (DCF) based methods have shown competitive performance on tracking benchmarks in recent years. Generally, DCF based trackers learn a rigid appeara…