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Impresee

e-Commerce Labs

We innovate in eCommerce solutions through applied research on AI and data science supported by a continuous collaboration of our ecosystem.

Our Research Area

We work on real eCommerce problems through innovative solutions based on computer vision, natural language processing, and data science. Our strategy relies on joint work with our partner stores, our scientific collaboration network, and our high-level research team.

Search & Navigation

We are developing new methods to improve product organization and display catalogs in a dynamic and personalized way.

Recommendation Systems

We are working on improved models to enhance recommendation quality and anticipate shopper preferences.

Other eCommerce dilema?

Tell us more and let’s deal with it

Data Analytics

We are exploring new ways to capture e-commerce data, get insights, and take effective actions.

Other eCommerce dilema?

Tell us more and let’s deal with it

Impresee collaboration

Partners

Impresee

Research Leads

José M. Saavedra

Director of Impresee Labs. PhD in Computer Science, University of Chile.

Research Areas: Computer Vision, Deep Learning, Pattern Recognition


Juan Barrios

CEO of Impresee. PhD in Computer Science, University of Chile.

Research Areas: Multimedia Information Retrieval, Computer Vision, Data mining,NLP

José M. Saavedra

José M. Saavedra

Director of Impresee Labs. PhD in Computer Science, University of Chile

 

Research Areas: Computer Vision, Deep Learning, Pattern Recognition

Anibal Fuentes

Anibal Fuentes

Impresee developer

 

Research Areas: Computer Vision, Machine Learning, Data Scientist


Camila Álvarez

CTO of Impresee, University of Chile.

Research Areas: Computer Vision, Data Scientist


Anibal Fuentes

Impresee Developer.

Research Areas: Machine Learning, Deep Learning, Data Scientist

Juan Barrios

Juan Barrios

Director of Impresee Labs. PhD in Computer Science, University of Chile

 

Research Areas: Computer Vision, Deep Learning, Pattern Recognition

Camila Álvarez

Camila Álvarez

CTO of Impresee

 

Research Areas: Computer Vision, Data Scientist

our

Alumni

Pablo Torres

Sketch based Retrieval

Catalina Contreras

Visual Recommendation

Guillermo Martínez

Semantic Content-based Image Retrieval

Jean Cherubini

One-shot detection

Javier Morales

Unsupervised Learning for SBIR

Lukas Pavez

Non-Local Features

Diego Donoso

Sketch2Photo

Andres Baloian

Attribute based Retrieval

Cristobal Loyola

One-Shot Detection

Gonzalo Mondaca

Self-Attention on Sketch-based Image Retrieval

our

Publications

 

2021

Pablo Torres, Jose M. Saavedra

Compact and Effective Representations for Sketch-based Image Retrieval

1st Workshop on Sketch-Oriented Deep Learning (SketchDL), CVPR 2021 [accepted]

 

Anibal Fuentes, Jose M. Saavedra

Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Image Retrieval

1st Workshop on Sketch-Oriented Deep Learning (SketchDL), CVPR 2021 [accepted]

Andres Baloian, Nils Murrugarra-Llerena, Jose M. Saavedra

Scalable Visual Attribute Extraction through Hidden Layers of a Residual ConvNet

LatinXinCV research workshop, CVPR 2021 [accepted as “extended abstract”]

2020

Andre G. Hochuli, Alceu S. Britto Jr., David A. Saji, Jose M. Saavedra, Robert Sabourin, Luiz S. Oliveira,

A Comprehensive Comparison of End-to-End Approaches for Handwritten Digit String Recognition.

Expert Systems with Applications, 2020. 

Úbeda, I., Saavedra, J.M., Nicolas, S., Petitjean, C., Heutte, L.

Improving Pattern Spotting in Historical Documents Using Feature Pyramid Networks

Pattern Recognition Letters,  2020.

2019

Fabian Souto Herrera, Jose M. Saavedra.

DLDENet: Deep Local Directional Embeddings with Increased Foreground Focal Loss for object detection

38th International Conference of the Chilean Computer Science Society (SCCC), 2019.

Úbeda, I., Saavedra, J.M.,Nicolas, S., Petitjean, C., Heutte, L.

Pattern spotting in historical documents using convolutional models

ACM International Conference Proceeding Series,  2019

 

2017

Domingo Mery , Erick Svec , Marco Arias , Vladimir Riffo , Jose M. Saavedra , Sandipan Banerjee.

Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection

IEEE Transactions on Systems Man Cybernetics-Systems, 2017  

 

Saavedra, Jose M.

RST-SHELO: Sketch-based Image Retrieval using Sketch Tokens and Square Root Normalization

Multimedia Tools and Applications, 2017

2016

Jose M. Saavedra, Camila Alvarez.

DeepSBIR: Sketch Based Image Retrieval using Deep Features

1st Workshop on Deep Learning for Pattern Recognition DLPR, ICPR, Cancún, Mexico, 2016.

2015

Barrios, J.M., Saavedra, J.M.

Score propagation based on Similarity Shot Graph for improving Visual Object Retrieval

SLAM 2015 – Proceedings of the 2015 Workshop on Speech, Language and Audio in Multimedia, co-located with ACM MM 2015 pp. 19-22. 2015.

 

Jose M. Saavedra and Juan Manuel Barrios.

Sketch based Image Retrieval using Learned KeyShapes

(LKS). In Proceedings of the British Machine Vision Conference (BMVC), 2015.

Felipe Ramírez, Héctor Contreras, Alejandro Figueroa,Mauricio Palma.

Gestión de Contactabilidad en Email para Mejorar la Experiencia del Usuario en la Industria Financiera

11o Seminario Internacional “Big Data” de la Fundación Copec, Universidad Católica, 2015.

See more

2014

Saavedra, Jose M., Bustos, Benjamin.

Sketch-based Image Retrieval using Keyshapes

. Multimedia Tools and Applications, 2014. 

DOI: 10.1007/s11042-013-1689-0

Chang, Violeta, Saavedra, Jose M., Castaneda, Victor, Sarabia, Luis, Hitschfeld, Nancy, Haertel, Steffen.

Gold-standard and improved framework for sperm head segmentation

Computer Methods and Programs In Biomedicine, 2014.

DOI: 10.1016/j.cmpb.2014.06.018

Li, Bo, Lu, Yijuan, Godil, Afzal, Schreck, Tobias, Bustos, Benjamin, Ferreira, Alfredo, Furuya, Takahiko, Fonseca, Manuel J., Johan, Henry, Matsuda, Takahiro, Ohbuchi, Ryutarou, Pascoal, Pedro B., Saavedra, Jose M.

A Comparison of Methods for Sketch-Based 3d Shape Retrieval

. Computer Vision and Image Understanding, 2014.

DOI: 10.1016/j.cviu.2013.11.008

Saavedra, Jose M. Handwritten Digit Recognition Based on Pooling SVM-Classifiers Using Orientation and Concavity Based Features. Lecture Notes in Computer Science, 2014.

DOI https://doi.org/10.1007/978-3-319-12568-8_80

Saavedra, Jose M.

Sketch Based Image Retrieval Using a Soft Computation of The Histogram of Edge Local Orientations (S-HELO)

. IEEE International Conference on Image Processing (ICIP),  2014.

DOI: 10.1109/ICIP.2014.7025606

Diem, Markus, Fiel, Stefan, Kleber, Florian, Sablatnig, Robert, Saavedra, Jose M., Contreras, David, Barrios, Juan Manuel, Oliveira, Luiz S. ICFHR 2014 Competition on Handwritten Digit String Recognition in Challenging Datasets (HDSRC 2014), 14TH International Conference on Frontiers in Handwriting Recognition (ICFHR), 2014.

DOI: 10.1109/ICFHR.2014.136

2013Juan Manuel Barrios, José M. Saavedra, Felipe Ramirez, David Contreras.

ORAND Team: Instance Search and Multimedia Event Detection Using k-NN Searches

.
TRECVID, 2013.

Saavedra, J.M., Bustos, B., Chang, V.

An accurate hand segmentation approach using a structure based shape localization technique

. VISAPP 2013 – Proceedings of the International Conference on Computer Vision Theory and Applications, 2013.

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878249062&partnerID=40&md5=ad06057a567587a282c88e7bae59b53f 

Li, B., Lu, Y., Godil, A.,Schreck, T., Aono, M., Johan, H., Saavedra, J.M., Tashiro, S.

Shrec’13 track: Large scale Sketch-based 3D Shape Retrieval

. 6th Eurographics Workshop on 3D Object Retrieval, 3DOR 2013,  2013.

DOI: 10.2312/3DOR/3DOR13/089-096

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