no code implementations • 12 May 2023 • David Anglada-Rotger, Ferran Marqués, Montse Pardàs
Results demonstrate that our final model outperforms the state-of-the-art image style transfer methods in maintaining the cell classes in the transformed images and is as effective as them in generating realistic images.
1 code implementation • 7 Oct 2022 • Andreu Girbau, Ferran Marqués, Shin'ichi Satoh
Current approaches in Multiple Object Tracking (MOT) rely on the spatio-temporal coherence between detections combined with object appearance to match objects from consecutive frames.
Ranked #10 on Multi-Object Tracking on MOT17
no code implementations • 21 Jun 2021 • Andreu Girbau, Xavier Giró-i-Nieto, Ignasi Rius, Ferran Marqués
In this work, we show that trajectory estimation can become a key factor for tracking, and present TrajE, a trajectory estimator based on recurrent mixture density networks, as a generic module that can be added to existing object trackers.
Ranked #25 on Multi-Object Tracking on MOT17 (MOTA metric)
no code implementations • 27 May 2015 • Carles Ventura, Xavier Giró-i-Nieto, Verónica Vilaplana, Kevin McGuinness, Ferran Marqués, Noel E. O'Connor
This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem.