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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 EMAIL,EMAIL, EMAIL EMAIL, EMAIL |
| 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. |