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20142026
most citedSLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks

116 citations · 477 across the 58 of their papers we have counts for

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Showing 2016 · cs.CVShow all

6 papers · 2 filters

cs.CV2016

VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering

Marc Bolaños, Álvaro Peris, Francisco Casacuberta +1

In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Ne…

cs.CV2016★ 5 cited

Can a CNN Recognize Catalan Diet?

Pedro Herruzo, Marc Bolaños, Petia Radeva

Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these disease…

cs.CV2016★ 16 cited

Generic Feature Learning for Wireless Capsule Endoscopy Analysis

Santi Seguí, Michal Drozdzal, Guillem Pascual +4

The interpretation and analysis of the wireless capsule endoscopy recording is a complex task which requires sophisticated computer aided decision (CAD) systems in order to help ph…

cs.CV2016

With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams

Maedeh Aghaei, Mariella Dimiccoli, Petia Radeva

Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely b…

cs.CV2016

Simultaneous Food Localization and Recognition

Marc Bolaños, Petia Radeva

The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concern…

cs.CV2016

Video Description using Bidirectional Recurrent Neural Networks

Álvaro Peris, Marc Bolaños, Petia Radeva +1

Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combin…