Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
UAWTrack: Universal 3D Single Object Tracking in Adverse Weather
Authors: Yuxiang Yang, Hongjie Gu, Yingqi Deng, Zhekang Dong, Zhiwei He, Jing Zhang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that UAWTrack achieves state-of-the-art performance under all weather conditions. |
| Researcher Affiliation | Academia | 1School of Electronics and Information, Hangzhou Dianzi University, China 2School of Computer Science, Wuhan University, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using textual explanations, mathematical equations, and diagrams. There are no explicit pseudocode or algorithm blocks labeled in the document. |
| Open Source Code | Yes | Code https://github.com/HDU-VRLab/UAWTrack |
| Open Datasets | Yes | Specifically, by applying physically-based adverse weather simulation algorithms (Hahner et al. 2021, 2022; Dong et al. 2023) to two classic autonomous driving datasets, KITTI (Geiger, Lenz, and Urtasun 2012) and Nu Scenes (Caesar et al. 2020), we create two synthetic datasets: KITTI-A and Nu Scenes A. |
| Dataset Splits | Yes | Specifically, KITTI-A contains 500 sequences, which are split into training (210 sequences) and testing sets (290 sequences), following the settings in previous works (Yang et al. 2023; Xu et al. 2023b). Compared to KITTI-A, Nu Scenes-A is a more challenging dataset which contains 7,000 and 1,500 scenes for training and testing, respectively. |
| Hardware Specification | Yes | Our model is trained with a batch size of 128 and an initial learning rate of 1 10 4 using the Adam W optimizer on two NVIDIA GTX 4090 GPUs. |
| Software Dependencies | No | The network is implemented in Py Torch with MMEngine (Contributors 2022). While PyTorch and MMEngine are mentioned, specific version numbers for these software components are not provided. |
| Experiment Setup | Yes | Our model is trained with a batch size of 128 and an initial learning rate of 1 10 4 using the Adam W optimizer on two NVIDIA GTX 4090 GPUs. |