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20172024
most citedThe 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation

100 citations · 118 across the 4 of their papers we have counts for

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6 papers · 1 filter

cs.CV2024

VISTA: A Visual and Textual Attention Dataset for Interpreting Multimodal Models

Harshit, Tolga Tasdizen

The recent developments in deep learning led to the integration of natural language processing (NLP) with computer vision, resulting in powerful integrated Vision and Language Mode…

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…

cs.CV20197 cited

Fast video object segmentation with Spatio-Temporal GANs

Sergi Caelles, Albert Pumarola, Francesc Moreno-Noguer +2

Learning descriptive spatio-temporal object models from data is paramount for the task of semi-supervised video object segmentation. Most existing approaches mainly rely on models…

cs.CV2018

Iterative Deep Learning for Road Topology Extraction

Carles Ventura, Jordi Pont-Tuset, Sergi Caelles +2

This paper tackles the task of estimating the topology of road networks from aerial images. Building on top of a global model that performs a dense semantical classification of the…

cs.CV2018

The 2018 DAVIS Challenge on Video Object Segmentation

Sergi Caelles, Alberto Montes, Kevis-Kokitsi Maninis +4

We present the 2018 DAVIS Challenge on Video Object Segmentation, a public competition specifically designed for the task of video object segmentation. It builds upon the DAVIS 201…

cs.CV201711 cited

Iterative Deep Learning for Network Topology Extraction

Carles Ventura, Jordi Pont-Tuset, Sergi Caelles +2

This paper tackles the task of estimating the topology of filamentary networks such as retinal vessels and road networks. Building on top of a global model that performs a dense se…