CDE-Learning: Camera Deviation Elimination Learning for Unsupervised Person Re-identification
Authors: Jinjia Peng, Songyu Zhang, Huibing Wang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrated the superior performance of the proposed CDE-Learning on benchmark datasets. Our proposed method is evaluated on reidentification benchmarks, namely Market-1501 (Zheng et al. 2015), MSMT17 (Wei et al. 2018), Person X (Sun and Zheng 2019), and CUHK03 (Li et al. 2014). |
| Researcher Affiliation | Academia | 1School of Cyber Security and Computer, Hebei University, Hebei, China Hebei Machine Vision Engineering Research Center, China 2College of Information Science and Technology, Dalian Maritime University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: CDE-Learning Inputs: Unlabeled dataset with camera labels Parameters: Sampling parameter p and k Output: The fine-tuned encoder and checkpoints Start: Initialize Epoch parameter num epochs and iterations parameter num iters while epoch in [1, num epochs] do Extract the features F with the encoder Construct camera domain {F1, F2, F3, . . . , Fµ} Obtain camera centroids {s1, s2, s3, . . . , sµ} by Eq.1 Obtain the global centroid sg by Eq.2 Align features Fi to get refined features F i by Eq.3 Clustering F = {F 1, F 2, F 3, . . . , F µ} into m clusters with DBSCAN while iter in [1, num iters] do Sample p k queries from pseudo labeled dataset Extract the minibatch features Q through the encoder Compute loss L = Lc + Ld + Lt Back propagation Update parameters of the encoder by optimizer Update multi-prototype memory by Eq. 5 Update centroid memory by Eq. 7 end while end while |
| Open Source Code | Yes | Code https://github.com/zsszyx/CDE-Learning |
| Open Datasets | Yes | Our proposed method is evaluated on reidentification benchmarks, namely Market-1501 (Zheng et al. 2015), MSMT17 (Wei et al. 2018), Person X (Sun and Zheng 2019), and CUHK03 (Li et al. 2014). |
| Dataset Splits | No | The paper mentions several benchmark datasets like Market-1501, MSMT17, Person X, and CUHK03, but does not explicitly provide details about the specific training/test/validation splits used in their experiments. It references the datasets by their original papers without stating how they were partitioned for the current work. |
| Hardware Specification | Yes | Our method is trained on an Nvidia A4000 GPU under the Py Torch framework. |
| Software Dependencies | No | The paper mentions the 'Py Torch framework' but does not specify any version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | Adam optimizer is utilized with weight decay 5e-4 to train our re ID model. The initial learning rate is set to 3.5e-4 for the first ten epochs with a warm-up scheme, after which it is decreased to 1/10 of its previous value every 20 epochs for 80 epochs. Table 8 presents a detailed examination of the outcomes associated with the temperature parameter τ in our method. The results indicate that this parameter s sensitivity is crucial in distinguishing between identities. Notably, our method attains its best performance at the τ = 0.05... Table 9 reflects the effects of PK sampling on the Re ID outcomes. Our method yields the most favorable results at the parameter setting of (16, 16)... |