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..
SJDL-Vehicle: Semi-supervised Joint Defogging Learning for Foggy Vehicle Re-identification
Authors: Wei-Ting Chen, I-Hsiang Chen, Chih-Yuan Yeh, Hao-Hsiang Yang, Jian-Jiun Ding, Sy-Yen Kuo347-355
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed method is effective and outperforms other existing vehicle Re ID methods in the foggy weather. |
| Researcher Affiliation | Collaboration | Wei-Ting Chen1,3 , I-Hsiang Chen2 , Chih-Yuan Yeh2, Hao-Hsiang Yang2, Jian-Jiun Ding2, and Sy-Yen Kuo2 1Graduate Institute of Electronics Engineering, National Taiwan University, Taiwan 2Department of Electrical Engineering, National Taiwan University, Taiwan 3ASUS Intelligent Cloud Services, Taiwan EMAIL |
| Pseudocode | No | The paper describes the proposed architecture and processes in prose but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and dataset are available in https://github.com/Cihsaing/SJDLFoggy-Vehicle-Re-Identification--AAAI2022. |
| Open Datasets | Yes | Due to the lack of a dataset specialized for vehicle Re ID in the foggy weather, we construct a dataset called FVRID which consists of real-world and synthetic foggy images to train and evaluate the performance. The code and dataset are available in https://github.com/Cihsaing/SJDLFoggy-Vehicle-Re-Identification--AAAI2022. |
| Dataset Splits | No | The paper provides details for 'Train', 'Probe', and 'Gallery' sets, but does not explicitly mention or detail a separate 'validation' dataset split for hyperparameter tuning or early stopping during training. 'Probe' is used for evaluation/testing. |
| Hardware Specification | Yes | The network is trained on an Nvidia Tesla V100 GPU for 20 hours and we implement it on the Pytorch platform. |
| Software Dependencies | No | The paper states 'we implement it on the Pytorch platform' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The input image is resized to 384 x 384 and the training batch size Q is set to 36. We apply horizontal flip and random crop to prevent the overfitting problem due to the limited number of training data. We train models for 120 epochs with a warm-up strategy. The initial learning rate is 1.09 x 10^-5, which increases to 10^-4 after the 10th epoch. The Adam optimizer is adopted to optimize the model with a decay rate of 0.6. The hyper-parameters λ1, λ2, λ3, and λ4 are set as to 1, 10^-5, 10^-5, and 300. |