DCCO: Towards Deformable Continuous Convolution Operators for Visual Tracking. - Joakim Johnander; Martin Danelljan; Fahad Shahbaz Khan; Michael Felsberg (CAIP (1), 2017)

DCCO: Towards Deformable Continuous Convolution Operators. - Joakim Johnander; Martin Danelljan; Fahad Shahbaz Khan; Michael Felsberg (CoRR, 2017)

Unveiling the Power of Deep Tracking. - Goutam Bhat; Joakim Johnander; Martin Danelljan; Fahad Shahbaz Khan; Michael Felsberg (ECCV (2), 2018)

On the Optimization of Advanced DCF-Trackers. - Joakim Johnander; Goutam Bhat; Martin Danelljan; Fahad Shahbaz Khan; Michael Felsberg (ECCV Workshops (1), 2018)

The Sixth Visual Object Tracking VOT2018 Challenge Results. - Matej Kristan; Ales Leonardis; Jiri Matas; Michael Felsberg; Roman P. Pflugfelder; Luka Cehovin Zajc; Tomás Vojír; Goutam Bhat; Alan Lukezic; Abdelrahman Eldesokey; Gustavo Fernández; Álvaro García-Martín; et al. (ECCV Workshops (1), 2018)

A Generative Appearance Model for End-To-End Video Object Segmentation. - Joakim Johnander; Martin Danelljan; Emil Brissman; Fahad Shahbaz Khan; Michael Felsberg (CVPR, 2019)

Semi-Automatic Annotation of Objects in Visual-Thermal Video. - Amanda Berg; Joakim Johnander; Flavie Durand de Gevigney; Jörgen Ahlberg; Michael Felsberg (ICCV Workshops, 2019)

Learning Video Instance Segmentation with Recurrent Graph Neural Networks. - Joakim Johnander; Emil Brissman; Martin Danelljan; Michael Felsberg (CoRR, 2020)

Video Instance Segmentation with Recurrent Graph Neural Networks. - Joakim Johnander; Emil Brissman; Martin Danelljan; Michael Felsberg (GCPR, 2021)

Predicting Signed Distance Functions for Visual Instance Segmentation. - Emil Brissman; Joakim Johnander; Michael Felsberg (SAIS, 2021)

Deep Gaussian Processes for Few-Shot Segmentation. - Joakim Johnander; Johan Edstedt; Martin Danelljan; Michael Felsberg; Fahad Shahbaz Khan (CoRR, 2021)

Dense Gaussian Processes for Few-Shot Segmentation. - Joakim Johnander; Johan Edstedt; Michael Felsberg; Fahad Shahbaz Khan; Martin Danelljan (ECCV (29), 2022)

Learning Future Object Prediction with a Spatiotemporal Detection Transformer. - Adam Tonderski; Joakim Johnander; Christoffer Petersson; Kalle Åström (CoRR, 2022)

Towards Trustworthy Multi-Modal Motion Prediction: Evaluation and Interpretability. - Sandra Carrasco Limeros; Sylwia Majchrowska; Joakim Johnander; Christoffer Petersson; Miguel Ángel Sotelo; David Fernández Llorca (CoRR, 2022)

Towards Explainable Motion Prediction using Heterogeneous Graph Representations. - Sandra Carrasco Limeros; Sylwia Majchrowska; Joakim Johnander; Christoffer Petersson; David Fernández Llorca (CoRR, 2022)

Dynamic Visual Learning. - Joakim Johnander (2022)

You can have your ensemble and run it too - Deep Ensembles Spread Over Time. - Isak Meding; Alexander Bodin; Adam Tonderski; Joakim Johnander; Christoffer Petersson; Lennart Svensson (ICCV (Workshops), 2023)

Raw or Cooked? Object Detection on RAW Images. - William Ljungbergh; Joakim Johnander; Christoffer Petersson; Michael Felsberg (SCIA (1), 2023)

Hinge-Wasserstein: Mitigating Overconfidence in Regression by Classification. - Ziliang Xiong; Abdelrahman Eldesokey; Joakim Johnander; Bastian Wandt; Per-Erik Forssén (CoRR, 2023)

Recurrent Graph Neural Networks for Video Instance Segmentation. - Emil Brissman; Joakim Johnander; Martin Danelljan; Michael Felsberg (Int. J. Comput. Vis., 2023)

Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks. - Ziliang Xiong; Arvi Jonnarth; Abdelrahman Eldesokey; Joakim Johnander; Bastian Wandt; Per-Erik Forssén (CVPR Workshops, 2024)

NeuroNCAP: Photorealistic Closed-Loop Safety Testing for Autonomous Driving. - William Ljungbergh; Adam Tonderski; Joakim Johnander; Holger Caesar; Kalle Åström; Michael Felsberg; Christoffer Petersson (ECCV (30), 2024)

Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs. - Sandra Carrasco Limeros; Sylwia Majchrowska; Joakim Johnander; Christoffer Petersson; Miguel Ángel Sotelo; David Fernández Llorca (CAAI Trans. Intell. Technol., 2024)

CATPlan: Loss-based Collision Prediction in End-to-End Autonomous Driving. - Ziliang Xiong; Shipeng Liu; Nathaniel Helgesen; Joakim Johnander; Per-Erik Forssén (CoRR, 2025)