A Multi-Constraint Similarity Learning with Adaptive Weighting for Visible-Thermal Person Re-Identification
Authors: Yongguo Ling, Zhiming Luo, Yaojin Lin, Shaozi Li
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two benchmark datasets demonstrate the superior performance of the proposed over the state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Department of Artificial Intelligence, Xiamen University, China 2School of Computer Science, Minnan Normal University, China |
| Pseudocode | No | The paper describes the method verbally but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements regarding the availability of its source code. |
| Open Datasets | Yes | We evaluate our proposed methods on two publicly available VT-Re ID datasets (SYSU-M001 [Wu et al., 2017] and Reg DB [Nguyen et al., 2017]). |
| Dataset Splits | No | The paper specifies training and testing sets for both datasets (e.g., 'The training set consists of 22,258 RGB images and 11,909 infrared images from 395 identities.' for SYSU-M001 and '206 identities for training and others for testing.' for Reg DB) but does not explicitly define a separate 'validation' split with specific counts or percentages. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running the experiments (e.g., GPU models, CPU types, memory). |
| Software Dependencies | No | The paper mentions the use of 'SGD optimizer' but does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | The training batch is set to 96 (48 RGB images and 48 infrared images from 6 person IDs) and 64 (32 RGB images and 32 infrared images from 8 person IDs) for SYSU-MM01 and Reg DB dataset, respectively. The input images are resized to 288 144 3 for both RGB and infrared images. We use the SGD optimizer for training with an initial learning rate of 0.01, and train the model for 80 epochs. We divide the learning rate by 10 after every 10 epochs. The weights α, β, and ω in Eq. 5 are set to 0.5, 1.0, and 0.2, respectively. The pair mining margin m in Eq. 8 is set to 0.2. λ in Eq. 11 is set to 0.5. The weights γ in Eq. 12 is set to 1. |