activity
20152025
most citedHierarchical Object Detection with Deep Reinforcement Learning

87 citations · 486 across the 27 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.CV2021

H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction

Eduard Ramon, Gil Triginer, Janna Escur +4

Recent learning approaches that implicitly represent surface geometry using coordinate-based neural representations have shown impressive results in the problem of multi-view 3D re…

cs.AI2021

Unsupervised Skill-Discovery and Skill-Learning in Minecraft

Juan José Nieto, Roger Creus, Xavier Giro-i-Nieto

Pre-training Reinforcement Learning agents in a task-agnostic manner has shown promising results. However, previous works still struggle in learning and discovering meaningful skil…

cs.MM2021

PixInWav: Residual Steganography for Hiding Pixels in Audio

Margarita Geleta, Cristina Punti, Kevin McGuinness +3

Steganography comprises the mechanics of hiding data in a host media that may be publicly available. While previous works focused on unimodal setups (e.g., hiding images in images,…

cs.CV2021★ 3 cited

SynthRef: Generation of Synthetic Referring Expressions for Object Segmentation

Ioannis Kazakos, Carles Ventura, Miriam Bellver +2

Recent advances in deep learning have brought significant progress in visual grounding tasks such as language-guided video object segmentation. However, collecting large datasets f…

cs.CV2021

Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data

Oscar Mañas, Alexandre Lacoste, Xavier Giro-i-Nieto +2

Remote sensing and automatic earth monitoring are key to solve global-scale challenges such as disaster prevention, land use monitoring, or tackling climate change. Although there…

cs.CV2021★ 16 cited

Can Everybody Sign Now? Exploring Sign Language Video Generation from 2D Poses

Lucas Ventura, Amanda Duarte, Xavier Giro-i-Nieto

Recent work have addressed the generation of human poses represented by 2D/3D coordinates of human joints for sign language. We use the state of the art in Deep Learning for motion…