1 code implementation • 14 Mar 2024 • Atif Belal, Akhil Meethal, Francisco Perdigon Romero, Marco Pedersoli, Eric Granger
Domain adaptation methods for object detection (OD) strive to mitigate the impact of distribution shifts by promoting feature alignment across source and target domains.
1 code implementation • 26 Sep 2023 • Atif Belal, Akhil Meethal, Francisco Perdigon Romero, Marco Pedersoli, Eric Granger
Given the use of prototypes, the number of parameters required for our PMT method does not increase significantly with the number of source domains, thus reducing memory issues and possible overfitting.
Multi-Source Unsupervised Domain Adaptation object-detection +2
1 code implementation • 9 Aug 2023 • Akhil Meethal, Eric Granger, Marco Pedersoli
One of the important bottlenecks in training modern object detectors is the need for labeled images where bounding box annotations have to be produced for each object present in the image.
Ranked #1 on Object Detection on VisDrone - 10% labeled data
1 code implementation • 15 Mar 2023 • Akhil Meethal, Eric Granger, Marco Pedersoli
Detecting objects in aerial images is challenging because they are typically composed of crowded small objects distributed non-uniformly over high-resolution images.
Ranked #1 on 2D Object Detection on VisDrone
1 code implementation • 1 Apr 2022 • Akhil Meethal, Marco Pedersoli, Zhongwen Zhu, Francisco Perdigon Romero, Eric Granger
Semi- and weakly-supervised learning have recently attracted considerable attention in the object detection literature since they can alleviate the cost of annotation needed to successfully train deep learning models.
no code implementations • 26 Nov 2021 • Akhil Meethal, Asharaf S, Sumitra S
Kernel based Deep Learning using multi-layer kernel machines(MKMs) was proposed by Y. Cho and L. K.
1 code implementation • 3 Dec 2019 • Akhil Meethal, Marco Pedersoli, Soufiane Belharbi, Eric Granger
Weakly supervised object localization is a challenging task in which the object of interest should be localized while learning its appearance.