Robust Multi-Modality Person Re-identification
Authors: Aihua Zheng, Zi Wang, Zihan Chen, Chenglong Li, Jin Tang3529-3537
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 {ahzheng214, ziwang1121, zhchen96, lcl1314}@foxmail.com, tangjin@ahu.edu.cn |
| 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 fine-tune the network. |