activity
20172021
most citedLearning From Long-Tailed Data With Noisy Labels

15 citations · 31 across the 7 of their papers we have counts for

collaborators

11 papers

cs.CV202115 cited

Learning From Long-Tailed Data With Noisy Labels

Shyamgopal Karthik, Jérome Revaud, Boris Chidlovskii

Class imbalance and noisy labels are the norm rather than the exception in many large-scale classification datasets. Nevertheless, most works in machine learning typically assume b…

cs.RO2021

Magnetic Field Sensing for Pedestrian and Robot Indoor Positioning

Leonid Antsfeld, Boris Chidlovskii

In this paper we address the problem of indoor localization using magnetic field data in two setups, when data is collected by (i) human-held mobile phone and (ii) by localization…

cs.CV20213 cited

Universal Domain Adaptation in Ordinal Regression

Boris Chidlovskii, Assem Sadek, Christian Wolf

We address the problem of universal domain adaptation (UDA) in ordinal regression (OR), which attempts to solve classification problems in which labels are not independent, but fol…

cs.RO2020

Learning Synthetic to Real Transfer for Localization and Navigational Tasks

Maxime Pietrantoni, Boris Chidlovskii, Tomi Silander

Autonomous navigation consists in an agent being able to navigate without human intervention or supervision, it affects both high level planning and low level control. Navigation i…

cs.LG20203 cited

Deep Smartphone Sensors-WiFi Fusion for Indoor Positioning and Tracking

Leonid Antsfeld, Boris Chidlovskii, Emilio Sansano-Sansano

We address the indoor localization problem, where the goal is to predict user's trajectory from the data collected by their smartphone, using inertial sensors such as accelerometer…

cs.CV2020

Adversarial Transfer of Pose Estimation Regression

Boris Chidlovskii, Assem Sadek

We address the problem of camera pose estimation in visual localization. Current regression-based methods for pose estimation are trained and evaluated scene-wise. They depend on t…