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20172026
most citedLearning Video Instance Segmentation with Recurrent Graph Neural Networks

8 citations · 12 across the 5 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

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…

cs.CV2026

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…

cs.CV20212 cited

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…

cs.CV20208 cited

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…

cs.CV2018

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…

cs.CV2018

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…