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Joakim Johnander

4 accepted papers

2024

NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving

William Ljungbergh*, Adam Tonderski, Joakim Johnander, Holger Caesar, Kalle Åström, Michael Felsberg +1
ECCV 2024poster

"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 of safety-critical scenarios. The simulator learns from sequences of real-world driving sensor data and enables reconfi…

Cited by 24Source ↗PDF ↗code ↗Scholar ↗
2022

Dense Gaussian Processes for Few-Shot Segmentation

Joakim Johnander, Johan Edstedt, Michael Felsberg, Fahad Shahbaz Khan, Martin Danelljan
ECCV 2022poster

"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 design a method that aggregates detailed information from the support set, while being robust to large variations in appear…

Cited by 37Source ↗PDF ↗code ↗Scholar ↗
2019

A Generative Appearance Model for End-To-End Video Object Segmentation

Joakim Johnander, Martin Danelljan, Emil Brissman, Fahad Shahbaz Khan, Michael Felsberg
CVPR 2019oral

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 to extensive fine-tuning of a convolutional neural network for this purpose. Besides being prohibitively expensive, thi…

Cited by 236PDF ↗Scholar ↗
2018

Unveiling the Power of Deep Tracking

Goutam Bhat, Joakim Johnander, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg
ECCV 2018poster

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 performance compared to methods solely based on handcrafted features. In this paper, we investigate this key issue and p…

Cited by 605Source ↗PDF ↗Scholar ↗