Knowledge Commons of Institute of Automation,CAS
SST-GAN: Single Sample-based Realistic Traffic Image Generation for Parallel Vision | |
Jiangong Wang1,2![]() ![]() ![]() ![]() ![]() | |
2022-11 | |
会议名称 | 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) |
会议日期 | 2022-10-08~2022-10-12 |
会议地点 | Macau, China |
摘要 | To improve their adaptability to various kinds of driving situations, deep learning-based vision algorithms need images from rare scenes, such as extreme weather conditions and traffic congestions. However, most datasets collected from physical driving environments are lack of such images, making vision models trained on these datasets do not work well in scarce scenes. Thus, we design an SST-GAN method for controllably generating realistic images of scarce driving scenes based on the framework of parallel vision. Trained on only a single sample, SST-GAN can produce hundreds of rare scene images from two directions: style transfer and content generation. Specifically, a transition retraining method is designed to transfer the weather and lighting styles from common scenes to scarce scenes, and a structural similarity index loss is used as reconstruction loss to guarantee the trained network can obtain more realistic content modification and generation during the image reconstruction. Experimental results show that SST-GAN outperforms the state-of-the-art method on expanding the amount of scarce scene images from both style and content. The method is highly adaptable and works flexibly on handling image generation problems for various types of rare scenes. |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
国重实验室规划方向分类 | 虚实融合与迁移学习 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51656 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Yutong Wang |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy 3.Qingdao Academy of Intelligent Industries 4.Macau University of Science and Technology |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Jiangong Wang,Yutong Wang,Yonglin Tian,et al. SST-GAN: Single Sample-based Realistic Traffic Image Generation for Parallel Vision[C],2022. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
SST-GAN_Single_Sampl(1971KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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