How to train your deep multi-object tracker Y Xu, A Osep, Y Ban, R Horaud, L Leal-Taixé, X Alameda-Pineda Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 224 | 2020 |
Transcenter: Transformers with dense queries for multiple-object tracking Y Xu, Y Ban, G Delorme, C Gan, D Rus, X Alameda-Pineda IEEE transactions on pattern analysis and machine intelligence, 2021 | 191* | 2021 |
Deepmot: A differentiable framework for training multiple object trackers Y Xu, Y Ban, X Alameda-Pineda, R Horaud arXiv preprint arXiv:1906.06618 10 (11), 2019 | 50 | 2019 |
CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification G Delorme, Y Xu, S Lathuilière, R Horaud, X Alameda-Pineda International Conference on Pattern Recognition, 2020 | 16 | 2020 |
Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive? Y Xu, L Chambon, M Chen, A Alahi, M Cord, P Perez International Conference on Robotics and Automation (ICRA), 2024 | 5 | 2024 |
Active Contrastive Set Mining for Robust Audio-Visual Instance Discrimination. H Xuan, Y Xu, S Chen, Z Wu, J Yang, Y Yan, X Alameda-Pineda IJCAI, 3643-3649, 2022 | 1 | 2022 |
Challenges of Using Real-World Sensory Inputs for Motion Forecasting in Autonomous Driving Y Xu, L Chambon, É Zablocki, M Chen, M Cord, P Pérez arXiv preprint arXiv:2306.09281, 2023 | | 2023 |
Learning-based Spatial and Angular Information Separation for Light Field Compression J Shi, Y Xu, C Guillemot arXiv preprint arXiv:2304.06322, 2023 | | 2023 |
Supplementary Material: How To Train Your Deep Multi-Object Tracker Y Xu, A Osep, Y Ban, R Horaud, L Leal-Taixé, X Alameda-Pineda | | 2019 |