|Topic:||IM2GPS: image retrieval for geolocalization|
|Supervisor:||doc. Georgios Tolias, Ph.D.|
|Announce as:||Diplomová práce, Bakalářská práce, Semestrální projekt|
|Description:||Given a large collection of geo-tagged images, e.g. street-view images, the goal is to build an image-based localization system which will predict the location at which an outdoor test image was taken.
The task will be handled as image retrieval where a large collection of images needs to be indexed based on visual representation extracted with deep learning models.Given a test image, the top retrieved images from the collection, along with their GPS-labels are used to predict the location of the test image. The goal is to exploit transformer architectures to facilitate such prediction.
|Bibliography:||G. Kordopatis-Zilos et al. ICMR 2021, Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation
Vo Jacobs Hays, ICCV 2017, Revisiting IM2GPS in the Deep Learning Era
T. Wayend et al. ECCV 2016, Planet-photo geolocation with convolutional neural networks
Radenovic Tolias Chum, PAMI 2019, Fine-tuning CNN Image Retrieval with No Human Annotation