Artificial intelligence, Machine learning, Gesture recognition, Gesture and Speech recognition are his primary areas of study. The study incorporates disciplines such as Pattern recognition and Natural language processing in addition to Artificial intelligence. His biological study spans a wide range of topics, including Variety and Categorization.
His Gesture recognition research is under the purview of Computer vision. His Gesture research focuses on Lexicon and how it relates to Temporal database, Human–computer interaction and Presentation. His Model selection study incorporates themes from Particle swarm optimization, Domain knowledge and Selection.
Hugo Jair Escalante mainly investigates Artificial intelligence, Machine learning, Data mining, Pattern recognition and Genetic programming. His Artificial intelligence research includes elements of Computer vision and Natural language processing. His study in State extends to Machine learning with its themes.
His study looks at the relationship between Data mining and topics such as Automatic image annotation, which overlap with Visual Word. His Genetic programming research integrates issues from Deep learning, Feature learning and Feature vector. The various areas that he examines in his Gesture study include Speech recognition and One-shot learning.
Hugo Jair Escalante spends much of his time researching Artificial intelligence, Machine learning, Data science, Field and Facial recognition system. Hugo Jair Escalante combines subjects such as Pattern recognition and Natural language processing with his study of Artificial intelligence. His study in the field of Text mining also crosses realms of Alphabetical order.
Hugo Jair Escalante has included themes like Representation, Feature extraction and State in his Machine learning study. His Data science research incorporates elements of Textual information and Authentication. His research in the fields of Anti spoofing overlaps with other disciplines such as Face.
His scientific interests lie mostly in Artificial intelligence, Machine learning, Field, Big Five personality traits and Modalities. His Artificial intelligence research is multidisciplinary, incorporating elements of Computer vision and Pattern recognition. His Pattern recognition study combines topics in areas such as Watershed and Facial expression.
His Genetic programming, Feature learning and Deep learning study, which is part of a larger body of work in Machine learning, is frequently linked to Everyday life and Order, bridging the gap between disciplines. His research integrates issues of Feature, Data science and Benchmark in his study of Facial recognition system. His work carried out in the field of Feature vector brings together such families of science as Heuristics, Data mining and Support vector machine.
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The segmented and annotated IAPR TC-12 benchmark
Hugo Jair Escalante;Carlos A. Hernández;Jesus A. Gonzalez;A. López-López.
Computer Vision and Image Understanding (2010)
Chalearn looking at people challenge 2014: Dataset and results
Sergio Escalera;Xavier Baró;Jordi Gonzàlez;Miguel Ángel Bautista.
european conference on computer vision (2014)
Multi-modal gesture recognition challenge 2013: dataset and results
Sergio Escalera;Jordi Gonzàlez;Xavier Baró;Miguel Reyes.
international conference on multimodal interfaces (2013)
Particle Swarm Model Selection
Hugo Jair Escalante;Manuel Montes;Luis Enrique Sucar.
Journal of Machine Learning Research (2009)
ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results
Sergio Escalera;Junior Fabian;Pablo Pardo;Xavier Baro.
international conference on computer vision (2015)
Taking Human out of Learning Applications: A Survey on Automated Machine Learning
Quanming Yao;Mengshuo Wang;Hugo Jair Escalante;Isabelle Guyon.
arXiv: Artificial Intelligence (2018)
A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences
Maryam Asadi-Aghbolaghi;Albert Clapes;Marco Bellantonio;Hugo Jair Escalante.
ieee international conference on automatic face gesture recognition (2017)
ChaLearn LAP 2016: First Round Challenge on First Impressions - Dataset and Results
Víctor Ponce-López;Víctor Ponce-López;Baiyu Chen;Marc Oliu;Ciprian A. Corneanu.
european conference on computer vision (2016)
Local Histograms of Character N-grams for Authorship Attribution
Hugo Jair Escalante;Thamar Solorio;Manuel Montes-y-Gomez.
meeting of the association for computational linguistics (2011)
ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016
Sergio Escalera;Sergio Escalera;Mercedes Torres Torres;Brais Martinez;Xavier Baro.
computer vision and pattern recognition (2016)
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