4 citations · 4 across the 7 of their papers we have counts for
7 papers · 1 filter
Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors
Atif Belal, Akhil Meethal, Francisco Perdigon Romero +2
Domain adaptation methods for object detection (OD) strive to mitigate the impact of distribution shifts by promoting feature alignment across source and target domains. Multi-sour…
Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher
Atif Belal, Akhil Meethal, Francisco Perdigon Romero +2
Adapting visual object detectors to operational target domains is a challenging task, commonly achieved using unsupervised domain adaptation (UDA) methods. Recent studies have show…
Density Crop-guided Semi-supervised Object Detection in Aerial Images
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 th…
Cascaded Zoom-in Detector for High Resolution Aerial Images
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. Density croppi…
Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth Boxes
Akhil Meethal, Marco Pedersoli, Zhongwen Zhu +2
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 suc…
Unsupervised MKL in Multi-layer Kernel Machines
Akhil Meethal, Asharaf S, Sumitra S
Kernel based Deep Learning using multi-layer kernel machines(MKMs) was proposed by Y.Cho and L.K. Saul in \cite{saul}. In MKMs they used only one kernel(arc-cosine kernel) at a lay…
Convolutional STN for Weakly Supervised Object Localization
Akhil Meethal, Marco Pedersoli, Soufiane Belharbi +1
Weakly supervised object localization is a challenging task in which the object of interest should be localized while learning its appearance. State-of-the-art methods recycle the…