Few-shot discriminative learning for object countingĐukić, Nikola (Avtor) Kristan, Matej (Mentor) computer visionfew-shot learningobject countingtransformersfew-shot object countingExisting few-shot object counting methods rely on matching the query image features with the features extracted from the exemplar objects. This approach lacks expressiveness since it relies solely on visual features of the exemplar objects. In this work, we propose an architecture that instead predicts an exemplar object model. Our method explicitly learns scale-dependent object priors and transforms them into the exemplar model using the transformer-based exemplar model predictor. The exemplar model predictor fuses the prior information with the information extracted from the exemplar objects and the the whole query image, thus combining the prior knowledge about objects in general with the class-specific object information, while also reasoning globally over the whole query image. With a minimal architectural change, our model can be modified into a zero-shot counting method. Our method sets new state-of-the-art in few-shot, one-shot and zero-shot counting with the relative improvements of 33.0 %, 33.6 %, 18.0 %, respectively, in terms of the FSC147 dataset test set MAE compared to the state-of-the-art methods.20222022-09-05 08:05:00Magistrsko delo/naloga139568VisID: 33741COBISS_ID: 121333763sl