Dimensionality reduction methods for representations generated by triplet convolutional networks

• Grant number: 17/10068-2
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: August 01, 2017
• End date: December 31, 2017
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Moacir Antonelli Ponti
• Grantee: Leo Sampaio Ferraz Ribeiro
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Siamese and triplet-loss-based nets, both variations of Convolutional Neural Networks (CNNs), are commonly applied to Content-based Image Retrieval and Domain Mapping systems thanks to their natural characteristic of generating feature spaces tuned for retrieval. There is however an improvement opportunity lying on the representation of the generated data; focusing on limited resources platforms such as mobile devices, it is of great interest that the feature vectors are compact and that retrieval itself is efficient. This project aims to explore such gap with experiments and studies focused on compact representation and efficient image retrieval on systems based on siamese and triplet networks. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

PONTI, MOACIR A.; RIBEIRO, LEONARDO S. F.; NAZARE, TIAGO S.; BUI, TU; COLLOMOSSE, JOHN; IEEE. Everything you wanted to know about Deep Learning for Computer Vision but were afraid to ask. 2017 30TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES TUTORIALS (SIBGRAPI-T), v. N/A, p. 25-pg., 2017-01-01. (13/07375-0, 17/10068-2, 15/04883-0)

BUI, TU; RIBEIRO, LEONARDO; PONTI, MOACIR; COLLOMOSSE, JOHN; JAWAHAR, CV; LI, H; MORI, G; SCHINDLER, K. Deep Manifold Alignment for Mid-Grain Sketch Based Image Retrieval. COMPUTER VISION - ACCV 2018, PT III, v. 11363, p. 16-pg., 2019-01-01. (17/10068-2, 16/16111-4, 13/07375-0)

BUI, TU; RIBEIRO, LEONARDO; PONTI, MOACIR; COLLOMOSSE, JOHN. Sketching out the details: Sketch-based image retrieval using convolutional neural networks with multi-stage regression. COMPUTERS & GRAPHICS-UK, v. 71, p. 77-87, APR 2018. (16/16111-4, 13/07375-0, 17/10068-2)