Search Results for author:
Found 6 papers, 4 papers with code
TAAT: Think and Act from Arbitrary Texts in Text2Motion
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.
Unleashing Unlabeled Data: A Paradigm for Cross-View Geo-Localization
This paper investigates the effective utilization of unlabeled data for large-area cross-view geo-localization (CVGL), encompassing both unsupervised and semi-supervised settings.
Pattern retrieval of traffic congestion using graph-based associations of traffic domain-specific features
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.
Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data?
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.
Towards Generic and Controllable Attacks Against Object Detection
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.
Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles
Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow.
