activity
20172022
most citedDetection-aided liver lesion segmentation using deep learning

35 citations · 107 across the 9 of their papers we have counts for

collaborators

12 papers

cs.LG2021

Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

Josep Lluis Berral, Oriol Aranda, Juan Luis Dominguez +1

Most research on novel techniques for 3D Medical Image Segmentation (MIS) is currently done using Deep Learning with GPU accelerators. The principal challenge of such technique is…

cs.CV20201 cited

RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation

Miriam Bellver, Carles Ventura, Carina Silberer +3

The task of video object segmentation with referring expressions (language-guided VOS) is to, given a linguistic phrase and a video, generate binary masks for the object to which t…

cs.CV2020

Mask-guided sample selection for Semi-Supervised Instance Segmentation

Miriam Bellver, Amaia Salvador, Jordi Torres +1

Image segmentation methods are usually trained with pixel-level annotations, which require significant human effort to collect. The most common solution to address this constraint…

cs.CV2020

How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language

Amanda Duarte, Shruti Palaskar, Lucas Ventura +5

One of the factors that have hindered progress in the areas of sign language recognition, translation, and production is the absence of large annotated datasets. Towards this end,…

cs.CV202032 cited

Improving accuracy and speeding up Document Image Classification through parallel systems

Javier Ferrando, Juan Luis Dominguez, Jordi Torres +5

This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essentia…

cs.LG2020

Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills

Víctor Campos, Alexander Trott, Caiming Xiong +3

Acquiring abilities in the absence of a task-oriented reward function is at the frontier of reinforcement learning research. This problem has been studied through the lens of empow…