3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022, Paris, France, 1 - 03 June 2022, vol.13363 LNCS, pp.109-120
© 2022, Springer Nature Switzerland AG.Query expansion is a standard technique in image retrieval, which enriches the original query by capturing various features from relevant images and further aggregating these features to create an expanded query. In this work, we present a new framework, which is based on incorporating uncertainty estimation on top of a self attention mechanism during the expansion procedure. An uncertainty network provides added information on the images that are relevant to the query, in order to increase the expressiveness of the expanded query. Experimental results demonstrate that integrating uncertainty information into a transformer network can improve the performance in terms of mean Average Precision (mAP) on standard image retrieval datasets in comparison to existing methods. Moreover, our approach is the first one that incorporates uncertainty in aggregation of information in a query expansion procedure.