5 papers
How Far is Too Far? Defining the Distance Threshold for Verification Siamese Networks
HeloÃsa Dias Viotto, Cauê Samonek, Lucas Garcia Pedroso +3
Siamese verification networks are widely used to compare items such as faces, cars, or signatures. In these scenarios, the network is trained to learn an embedding space in which s…
Toward Parking Spot Occupancy Recognition: A Self-Supervised Approach
Luan Marko Kujavski, Rayson Laroca, Paulo Lisboa de Almeida
As urban areas expand, automatic monitoring of parking lots becomes essential for efficient and sustainable cities. This work proposes a self-supervised approach for parking spot o…
Maintaining Difficulty: A Margin Scheduler for Triplet Loss in Siamese Networks Training
Roberto Sprengel Minozzo Tomchak, Oge Marques, Lucas Garcia Pedroso +2
The Triplet Margin Ranking Loss is one of the most widely used loss functions in Siamese Networks for solving Distance Metric Learning (DML) problems. This loss function depends on…
Using Deep Neural Networks to Quantify Parking Dwell Time
Marcelo Eduardo Marques Ribas, Heloisa Benedet Mendes, Luiz Eduardo Soares de Oliveira +2
In smart cities, it is common practice to define a maximum length of stay for a given parking space to increase the space's rotativity and discourage the usage of individual transp…
Optimizing Parking Space Classification: Distilling Ensembles into Lightweight Classifiers
Paulo Luza Alves, André Hochuli, Luiz Eduardo de Oliveira +1
When deploying large-scale machine learning models for smart city applications, such as image-based parking lot monitoring, data often must be sent to a central server to perform c…