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 [1].
Base-Detail Feature Learning Framework for Visible-Infrared Person Re-Identification
Authors: Zhihao Gong, Lian Wu, Yong Xu
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments conducted on the SYSUMM01, Reg DB, and LLCM datasets validate the effectiveness of BDLF. |
| Researcher Affiliation | Academia | Zhihao Gong1 , Lian Wu2 , Yong Xu1, 1Harbin Institute of Technology (Shenzhen) 2Gui Zhou Education University EMAIL, wulian EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using mathematical equations and textual explanations, but it does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide a link to a code repository. |
| Open Datasets | Yes | Comprehensive experiments conducted on the SYSUMM01, Reg DB, and LLCM datasets validate the effectiveness of BDLF. SYSU-MM01 dataset [Wu et al., 2017] comprises 287,628 VIS and 15,792 IR images from 491 identities captured by 4 RGB and 2 IR cameras. It features both All-Search and Indoor-Search modes for evaluation. Reg DB [Nguyen et al., 2017] contains 412 identities, each represented by 10 VIS and 10 IR images captured from a pair of cameras. We adhere to the evaluation protocol outlined in [Ye et al., 2022b] to randomly split the identities into training and testing sets of equal size. LLCM [Zhang and Wang, 2023]is a challenging large-scale low-light dataset for VI-Re ID task, which contains 713 identities with 25,626 VIS and 21,141 IR images, all captured by 9 cameras in both RGB and IR modalities |
| Dataset Splits | Yes | We adhere to the evaluation protocol outlined in [Ye et al., 2022b] to randomly split the identities into training and testing sets of equal size. For each mini-batch, we randomly sampled 8 identities, each consisting of 4 VIS and 4 IR images for training. |
| Hardware Specification | Yes | The entire framework is implemented using Py Torch and runs on a single NVIDIA RTX3090 GPU with 24GB VRAM. |
| Software Dependencies | No | We employed a pre-trained Res Net-50[He et al., 2016b] as the backbone network and incorporated INN blocks with affine coupling layers[Dinh et al., 2017][Zhou et al., 2022] to construct the DFE module, setting the number of INN blocks to 6. The SGD optimizer was used |
| Experiment Setup | Yes | The SGD optimizer was used, with the initial learning rate set to 1 10 2, which was warmed up to 1 10 1 during the first 10 epochs, then we decayed the learning rate to 1 10 2 and 1 10 3 at epochs 20 and 95 for SYSU-MM01, and at epochs 70 and 130 for Reg DB and LLCM, respectively. The learning rate was further decayed to 1 10 4 at 180 epoch, with a total of 220 epochs. For each mini-batch, we randomly sampled 8 identities, each consisting of 4 VIS and 4 IR images for training. Additionally, the exponential moving average (EMA) model [Ge et al., 2020] also employed in our method. |