activity
20162020
most citedInteractive Video Object Segmentation in the Wild

34 citations · 37 across the 3 of their papers we have counts for

collaborators

10 papers

cs.LG2020

Towards Reusable Network Components by Learning Compatible Representations

Michael Gygli, Jasper Uijlings, Vittorio Ferrari

This paper proposes to make a first step towards compatible and hence reusable network components. Rather than training networks for different tasks independently, we adapt the tra…

cs.CV2019

Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections

Theodora Kontogianni, Michael Gygli, Jasper Uijlings +1

In interactive object segmentation a user collaborates with a computer vision model to segment an object. Recent works employ convolutional neural networks for this task: Given an…

cs.CV20192 cited

Natural Vocabulary Emerges from Free-Form Annotations

Jordi Pont-Tuset, Michael Gygli, Vittorio Ferrari

We propose an approach for annotating object classes using free-form text written by undirected and untrained annotators. Free-form labeling is natural for annotators, they intuiti…

cs.CV2019

Efficient Object Annotation via Speaking and Pointing

Michael Gygli, Vittorio Ferrari

Deep neural networks deliver state-of-the-art visual recognition, but they rely on large datasets, which are time-consuming to annotate. These datasets are typically annotated in t…

cs.CV2018

Fast Object Class Labelling via Speech

Michael Gygli, Vittorio Ferrari

Object class labelling is the task of annotating images with labels on the presence or absence of objects from a given class vocabulary. Simply asking one yes/no question per class…

cs.CV2018

PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation

Ana García del Molino, Michael Gygli

Highlight detection models are typically trained to identify cues that make visual content appealing or interesting for the general public, with the objective of reducing a video t…