3 citations · 4 across the 2 of their papers we have counts for
6 papers
Depth Pruning with Auxiliary Networks for TinyML
Josen Daniel De Leon, Rowel Atienza
Pruning is a neural network optimization technique that sacrifices accuracy in exchange for lower computational requirements. Pruning has been useful when working with extremely co…
Improving Model Generalization by Agreement of Learned Representations from Data Augmentation
Rowel Atienza
Data augmentation reduces the generalization error by forcing a model to learn invariant representations given different transformations of the input image. In computer vision, on…
Data Augmentation for Scene Text Recognition
Rowel Atienza
Scene text recognition (STR) is a challenging task in computer vision due to the large number of possible text appearances in natural scenes. Most STR models rely on synthetic data…
GOO: A Dataset for Gaze Object Prediction in Retail Environments
Henri Tomas, Marcus Reyes, Raimarc Dionido +5
One of the most fundamental and information-laden actions humans do is to look at objects. However, a survey of current works reveals that existing gaze-related datasets annotate o…
Vision Transformer for Fast and Efficient Scene Text Recognition
Rowel Atienza
Scene text recognition (STR) enables computers to read text in natural scenes such as object labels, road signs and instructions. STR helps machines perform informed decisions such…
Next-Best View Policy for 3D Reconstruction
Daryl Peralta, Joel Casimiro, Aldrin Michael Nilles +3
Manually selecting viewpoints or using commonly available flight planners like circular path for large-scale 3D reconstruction using drones often results in incomplete 3D models. R…