Unsupervised Multi-Target Domain Adaptation for Object Detection
FPV@IPLAB - Department of Mathematics and Computer Science, University of Catania, Italy
We propose a dataset of synthetic and real images related to 16 artworks present in "Galleria regionale Palazzo Bellomo" located in Siracusa, Italy. The dataset contains two set of images, synthetic and real which are divided has follows:
Real Hololens Dataset
Real GoPro Dataset
You can download the whole dataset and annotations at this link
We explore the following methods:
1) baseline approaches without adaption;
2) domain adaptation through image to image translation;
3) domain adaptation through feature alignment;
4) new multi target domain adaptation method MDA-RetinaNet (see the figure below);
5) domain adaptation combining feature alignment and image to image translation.
G. Pasqualino, A. Furnari, G. M. Farinella, "Unsupervised Multi-Target Domain Adaptation for Object Detection", Submitted to International Conference on Image Processing 2021.
This research has been supported by the project VALUE (N. 08CT6209090207 - CUP G69J18001060007) - PO FESR 2014/2020 - Azione 1.1.5., by Research Program Pia.ce.ri. 2020/2022 Linea 2 - University of Catania, and by MIUR AIM - Linea 1 - AIM1893589 - CUP E64118002540007.