S
Sandra Avila
Researcher at State University of Campinas
Publications - 96
Citations - 3109
Sandra Avila is an academic researcher from State University of Campinas. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 24, co-authored 91 publications receiving 2224 citations. Previous affiliations of Sandra Avila include Universidade Federal de Minas Gerais & Pierre-and-Marie-Curie University.
Papers
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Journal ArticleDOI
VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method
TL;DR: VSUMM is presented, a methodology for the production of static video summaries that is based on color feature extraction from video frames and k-means clustering algorithm and develops a novel approach for the evaluation of video static summaries.
Journal ArticleDOI
Pooling in image representation: The visual codeword point of view
TL;DR: B BossaNova is proposed, a novel representation for content-based concept detection in images and videos, which enriches the Bag-of-Words model, and is compact and simple to compute.
Journal ArticleDOI
Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association
TL;DR: It is shown that for grape wines, a crop presenting large variability in shape, color, size and compactness, grape clusters can be successfully detected, segmented and tracked using state-of-the-art CNNs.
Book ChapterDOI
Data Augmentation for Skin Lesion Analysis
TL;DR: The results confirm the importance of data augmentation in both training and testing and show that it can lead to more performance gains than obtaining new images.
Proceedings ArticleDOI
Knowledge transfer for melanoma screening with deep learning
Afonso Menegola,Michel Fornaciali,Ramon Pires,Flávia Vasques Bittencourt,Sandra Avila,Eduardo Valle +5 more
TL;DR: In this paper, the authors investigate the presence of transfer, from which task the transfer is sourced, and the application of fine tuning (i.e., retraining of the deep learning model after transfer) and also test the impact of picking deeper (and more expensive) models.