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..
GenSeg: On Generating Unified Adversary for Segmentation
Authors: Yuxuan Zhang, Zhenbo Shi, Wei Yang, Shuchang Wang, Shaowei Wang, Yinxing Xue
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of Gen Seg in black-box attacks compared with state-of-the-art attacks. To evaluate the effectiveness of Gen Seg, we conduct comprehensive experiments on SS, IS, and PS, respectively. We employ 9 datasets and 15 models in total to validate our method. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Technology, University of Science and Technology of China 2 Suzhou Institute for Advanced Research, University of Science and Technology of China 3 Hefei National Laboratory, University of Science and Technology of China 4 Institute of Artificial Intelligence and Blockchain, Guangzhou University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Procedures are described using mathematical formulas and descriptive text. |
| Open Source Code | Yes | Code is available at: https://github.com/YXZhang979/Gen Seg |
| Open Datasets | Yes | To evaluate the attack effectiveness of Gen Seg, we employ the commonly used Pascal VOC (20 classes) [Everingham et al., 2010], Cityscapes (19 classes) [Cordts et al., 2016], and ADE20K (150 classes) [Zhou et al., 2017] for SS. For IS, we use the widely used City Scapes (8 things), COCO (80 things) [Lin et al., 2014], and ADE20K (100 things). As to PS, we adopt COCO (80 things and 53 stuff), Cityscapes (8 things and 11 stuff), and ADE20K (100 things and 50 stuff). |
| Dataset Splits | No | The paper mentions using well-known datasets and training models, but it does not explicitly provide specific percentages, sample counts, or predefined citations for training/validation/test dataset splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using specific models and optimizers (e.g., Adam optimizer, ResNet-based model) but does not provide specific software dependencies with version numbers (e.g., 'PyTorch 1.9', 'CUDA 11.1'). |
| Experiment Setup | Yes | We use the Adam optimizer with a learning rate of 5e-3 (β1 = .5, β2 = .999) for 100 epochs. We set the perturbation budget ϵ to the typical value of 8/255. Besides, we set attack iteration to 5, striking a balance between attack capability and efficiency. |