activity
20172022
most citedThe 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation

100 citations · 149 across the 7 of their papers we have counts for

collaborators

12 papers

cs.CV2021

Panoptic Narrative Grounding

C. González, N. Ayobi, I. Hernández +3

This paper proposes Panoptic Narrative Grounding, a spatially fine and general formulation of the natural language visual grounding problem. We establish an experimental framework…

cs.CV20211 cited

PanGEA: The Panoramic Graph Environment Annotation Toolkit

Alexander Ku, Peter Anderson, Jordi Pont-Tuset +1

PanGEA, the Panoramic Graph Environment Annotation toolkit, is a lightweight toolkit for collecting speech and text annotations in photo-realistic 3D environments. PanGEA immerses…

cs.CV2021

Telling the What while Pointing to the Where: Multimodal Queries for Image Retrieval

Soravit Changpinyo, Jordi Pont-Tuset, Vittorio Ferrari +1

Most existing image retrieval systems use text queries as a way for the user to express what they are looking for. However, fine-grained image retrieval often requires the ability…

cs.CV2019

Connecting Vision and Language with Localized Narratives

Jordi Pont-Tuset, Jasper Uijlings, Soravit Changpinyo +2

We propose Localized Narratives, a new form of multimodal image annotations connecting vision and language. We ask annotators to describe an image with their voice while simultaneo…

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.CV2019100 cited

The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation

Sergi Caelles, Jordi Pont-Tuset, Federico Perazzi +3

We present the 2019 DAVIS Challenge on Video Object Segmentation, the third edition of the DAVIS Challenge series, a public competition designed for the task of Video Object Segmen…