Bayesian Decision Making to Localize Visual Queries in 2D
Syed Asjad Aniket Gupta Hanumant Singh
This report describes our approach for the EGO4D 2023 Visual Query 2D
Localization Challenge. Our method aims to reduce the number of False Positives
(FP) that occur because of high similarity between the visual crop and the
proposed bounding boxes from the baseline's Region Proposal Network (RPN). Our
method uses a transformer to determine similarity in higher dimensions which is
used as our prior belief. The results are then combined together with the
similarity in lower dimensions from the Siamese Head, acting as our
measurement, to generate a posterior which is then used to determine the final
similarity of the visual crop with the proposed bounding box. Our code is
publicly available $\href{https://github.com/s-m-asjad/EGO4D_VQ2D}{here}$.