6 citations · 11 across the 6 of their papers we have counts for
7 papers
Multi-Level Feature Distillation of Joint Teachers Trained on Distinct Image Datasets
Adrian Iordache, Bogdan Alexe, Radu Tudor Ionescu
We propose a novel teacher-student framework to distill knowledge from multiple teachers trained on distinct datasets. Each teacher is first trained from scratch on its own dataset…
Learning Diverse Features in Vision Transformers for Improved Generalization
Armand Mihai Nicolicioiu, Andrei Liviu Nicolicioiu, Bogdan Alexe +1
Deep learning models often rely only on a small set of features even when there is a rich set of predictive signals in the training data. This makes models brittle and sensitive to…
JEDI: Joint Expert Distillation in a Semi-Supervised Multi-Dataset Student-Teacher Scenario for Video Action Recognition
Lucian Bicsi, Bogdan Alexe, Radu Tudor Ionescu +1
We propose JEDI, a multi-dataset semi-supervised learning method, which efficiently combines knowledge from multiple experts, learned on different datasets, to train and improve th…
A realistic approach to generate masked faces applied on two novel masked face recognition data sets
Tudor Mare, Georgian Duta, Mariana-Iuliana Georgescu +4
The COVID-19 pandemic raises the problem of adapting face recognition systems to the new reality, where people may wear surgical masks to cover their noses and mouths. Traditional…
Detecting abnormal events in video using Narrowed Normality Clusters
Radu Tudor Ionescu, Sorina Smeureanu, Marius Popescu +1
We formulate the abnormal event detection problem as an outlier detection task and we propose a two-stage algorithm based on k-means clustering and one-class Support Vector Machine…
How hard can it be? Estimating the difficulty of visual search in an image
Radu Tudor Ionescu, Bogdan Alexe, Marius Leordeanu +3
We address the problem of estimating image difficulty defined as the human response time for solving a visual search task. We collect human annotations of image difficulty for the…