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..
Robust Multi-Modality Person Re-identification
Authors: Aihua Zheng, Zi Wang, Zihan Chen, Chenglong Li, Jin Tang3529-3537
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on RGBNT201 dataset comparing to the state-of-the-art methods demonstrate the contribution of multi-modality person Re-ID and the effectiveness of the proposed approach, which launch a new benchmark and a new baseline for multi-modality person Re-ID. |
| Researcher Affiliation | Academia | Aihua Zheng, Zi Wang , Zihan Chen , Chenglong Li , Jin Tang Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the network architecture and various modules, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific link or statement indicating that the source code for the methodology is openly available or will be released. |
| Open Datasets | No | The paper introduces a new dataset, RGBNT201, stating 'we contribute a comprehensive benchmark dataset, RGBNT201'. While it's presented as a benchmark, no URL, DOI, or specific access instructions for this dataset are provided within the paper text. |
| Dataset Splits | Yes | We select 141 identities for training, 30 identities for validation, while the remaining 30 identities for testing. |
| Hardware Specification | Yes | The implementation platform is Pytorch with a NVIDIA GTX 1080Ti GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not specify its version or any other software dependencies with version numbers (e.g., specific library versions or operating system). |
| Experiment Setup | Yes | The initial learning rate is set as 1e-3. Consequently, we increase the number of train iterations due to the small learning rate. The number of mini-batches is 8. In the training phase, we use Stochastic Gradient Descent (SGD) with the momentum of 0.9 and weight decay of 0.0005 to ο¬ne-tune the network. |