no code implementations • 23 Apr 2024 • Runqi Wang, Caoyuan Ma, Guopeng Li, Zheng Wang
In this challenging dataset, we benchmark existing state-of-the-art methods and propose a novel two-stage framework to extract action labels from arbitrary texts by the Large Language Model (LLM) and then generate motions from action labels.
1 code implementation • 21 Mar 2024 • Guopeng Li, Ming Qian, Gui-Song Xia
This paper investigates the effective utilization of unlabeled data for large-area cross-view geo-localization (CVGL), encompassing both unsupervised and semi-supervised settings.
no code implementations • 28 Nov 2023 • Tin T. Nguyen, Simeon C. Calvert, Guopeng Li, Hans van Lint
To effectively interpret retrieval outcomes, the paper proposes a graph-based approach (relation-graph) for the former component, in which fundamental traffic phenomena are encoded as nodes and their spatiotemporal relationships as edges.
1 code implementation • 31 Oct 2023 • Guopeng Li, Victor L. Knoop, J. W. C., van Lint
To answer this, we propose an uncertainty-aware traffic forecasting framework to explore how many samples of loop data are truly effective for training forecasting models.
1 code implementation • 23 Jul 2023 • Guopeng Li, Yue Xu, Jian Ding, Gui-Song Xia
To this end, we propose a generic white-box attack, LGP (local perturbations with adaptively global attacks), to blind mainstream object detectors with controllable perturbations.
1 code implementation • 30 May 2023 • Guopeng Li, Yiru Jiao, Victor L. Knoop, Simeon C. Calvert, J. W. C. van Lint
Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow.