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20022026
most citedFitNets: Hints for Thin Deep Nets

2k citations

Showing 2016Show all

6 papers · 1 filter

cs.CV2016★ 25 cited

Scale Coding Bag of Deep Features for Human Attribute and Action Recognition

Fahad Shahbaz Khan, Joost van de Weijer, Rao Muhammad Anwer +3

Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a singl…

cs.CV2016★ 68 cited

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

Simon Jégou, Michal Drozdzal, David Vazquez +2

State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampli…

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

A fine-grained approach to scene text script identification

Lluis Gomez, Dimosthenis Karatzas

This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condi…

cs.CV2016★ 1 cited

Improving patch-based scene text script identification with ensembles of conjoined networks

Lluis Gomez, Anguelos Nicolaou, Dimosthenis Karatzas

This paper focuses on the problem of script identification in scene text images. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to a…

cs.GR2016★ 41 cited

Which tone-mapping operator is the best? A comparative study of perceptual quality

Xim Cerdá-Company, C. Alejandro Párraga, Xavier Otazu

Tone-mapping operators (TMO) are designed to generate perceptually similar low-dynamic range images from high-dynamic range ones. We studied the performance of fifteen TMOs in two…