no code implementations • 12 Jul 2023 • Jinglei Shi, Yihong Xu, Christine Guillemot
Light field is a type of image data that captures the 3D scene information by recording light rays emitted from a scene at various orientations.
1 code implementation • 15 Jun 2023 • Yihong Xu, Loïck Chambon, Éloi Zablocki, Mickaël Chen, Alexandre Alahi, Matthieu Cord, Patrick Pérez
In fact, conventional forecasting methods are usually not trained nor tested in real-world pipelines (e. g., with upstream detection, tracking, and mapping modules).
no code implementations • 13 Apr 2023 • Jinglei Shi, Yihong Xu, Christine Guillemot
Light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations.
no code implementations • 11 Mar 2022 • Zhuoran Song, Yihong Xu, Han Li, Naifeng Jing, Xiaoyao Liang, Li Jiang
The training phases of Deep neural network~(DNN) consumes enormous processing time and energy.
1 code implementation • 9 Mar 2022 • Zhuoran Song, Yihong Xu, Zhezhi He, Li Jiang, Naifeng Jing, Xiaoyao Liang
We explore the sparsity in ViT and observe that informative patches and heads are sufficient for accurate image recognition.
2 code implementations • 28 Mar 2021 • Yihong Xu, Yutong Ban, Guillaume Delorme, Chuang Gan, Daniela Rus, Xavier Alameda-Pineda
Methodologically, we propose the use of image-related dense detection queries and efficient sparse tracking queries produced by our carefully designed query learning networks (QLN).
Ranked #13 on Multi-Object Tracking on MOT20 (using extra training data)
2 code implementations • CVPR 2020 • Yihong Xu, Aljosa Osep, Yutong Ban, Radu Horaud, Laura Leal-Taixe, Xavier Alameda-Pineda
In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers.
Ranked #4 on Multi-Object Tracking on 2D MOT 2015
no code implementations • 2 Apr 2019 • Guillaume Delorme, Yihong Xu, Stephane Lathuilière, Radu Horaud, Xavier Alameda-Pineda
Unsupervised person re-ID is the task of identifying people on a target data set for which the ID labels are unavailable during training.