Feature-Level Adversarial Attacks and Ranking Disruption for Visible-Infrared Person Re-identification
Authors: Xi Yang, Huanling Liu, De Cheng, Nannan Wang, Xinbo Gao
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two VIRe ID benchmarks (i.e., SYSU-MM01, Reg DB) and different systems validate the effectiveness of our method. and 4 Experiments |
| Researcher Affiliation | Academia | Xi Yang1, Huanling liu1, De Cheng1 , Nannan Wang1, Xinbo Gao2 1Xidian University, 2Chongqing University of Posts and Telecommunications yangx@xidian.edu.cn,huanlingliu@stu.xidian.edu.cn, dcheng@xidian.edu.cn nnwang@xidian.edu.cn, gaoxb@cqupt.edu.cn |
| Pseudocode | No | The paper describes the method using text and figures, but does not include a formal pseudocode or algorithm block. |
| Open Source Code | No | Justification: We will consider making the code details publicly available in the future. |
| Open Datasets | Yes | SYSU-MM01[1] is a cross-modality pedestrian re-identification dataset proposed in 2017. and The Reg DB [43]dataset comprises 412 individuals, with each person having 20 images, including 10 visible images and 10 infrared images. |
| Dataset Splits | No | During training, we randomly sampled 16 identities, each with 4 images, to form a minibatch of size 64. The paper describes datasets and training/testing phases but does not explicitly provide percentages or counts for train/validation/test splits. |
| Hardware Specification | Yes | The experiments are conducted on an NVIDIA Ge Force 3090 GPU with Pytorch. |
| Software Dependencies | No | The experiments are conducted on an NVIDIA Ge Force 3090 GPU with Pytorch. Pytorch is mentioned without a version number, and no other specific software dependencies with versions are provided. |
| Experiment Setup | Yes | During training, we randomly sampled 16 identities, each with 4 images, to form a minibatch of size 64. Pedestrian images are resized to 288 144. Data augmentation included random horizontal flipping and random erasing with a probability of 0.5. We optimize using the stochastic gradient descent (SGD) optimizer, with a weight decay set to 0.0005 and a momentum parameter set to 0.9. The initial learning rate for both datasets was set to 0.1, and it was decayed by a factor of 0.1 at the 20th and 50th epochs, respectively. Finally, the margin α in the auxiliary quadruplet adversarial loss function is set to 0.2. We adopt a warm-up learning rate scheme, with a total of 60 training epochs. |