Adversarial Feature Disentanglement for Long-Term Person Re-identification
Authors: Wanlu Xu, Hong Liu, Wei Shi, Ziling Miao, Zhisheng Lu, Feihu Chen
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on six person Re-ID datasets, including three large-scale long-term datasets: Celeb-re ID, Celeb-re ID-light, PRCC, one benchmark short-term dataset: Market-1501, and two collected long-term datasets: Market Clothes, PKU-Market-Reid which are shown in Fig. 3. |
| Researcher Affiliation | Academia | 1Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University, China 2Department of Precision Instrument, Tsinghua University, China |
| Pseudocode | No | The paper describes the proposed method using textual explanations and mathematical equations, but it does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about making its source code publicly available or provide any links to a code repository. |
| Open Datasets | Yes | Moreover, we collect a challenging Market-Clothes dataset and a real-world PKU-Market-Reid dataset for evaluation. The results on one large-scale short-term dataset (Market-1501) and five long-term datasets (three public and two we proposed) confirm the superiority of our method against other state-of-the-art methods. |
| Dataset Splits | Yes | Celeb-re ID [Huang et al., 2020]... It has 20,208 images from 632 identities for training and 13,978 images from 420 identities for testing. PRCC [Yang et al., 2020]... The dataset has 150 and 71 identities for training and testing... Market-1501 [Zheng et al., 2015]... The dataset is split into two non-overlapping fixed parts: 12,936 images from 751 identities for training and 19,732 images from 750 identities for testing. |
| Hardware Specification | Yes | We implement the AFD-Net using Py Torch with only one NVIDIA GTX 1080Ti GPU. |
| Software Dependencies | No | The paper states "We implement the AFD-Net using Py Torch" but does not specify the version number of PyTorch or any other software dependencies. |
| Experiment Setup | Yes | All images are resized to 256 128 for input. The SGD optimizer is used to train Ei, Ec with lr = 0.002, momentum = 0.9. The Adam optimizer is applied to optimize G, D with lr = 0.0001, (β1, β2) = (0, 0.999). Following [Huang et al., 2018], we use a large weight λimg rec = 5 for the image reconstruction loss. The feature reconstruction loss is inaccurate in the initial stage, so we gradually increase λfea rec from 0 to 1 during the training process. Similarly, the identification loss Lrec id may make the training unstable due to the low quality of adversarial generated images at the beginning, so we set a small weight λrec id = 0.5. The remaining weights λraw id and λadv are set to 1. |