Knowledge Commons of Institute of Automation,CAS
FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation | |
Jie Qin1,2,3![]() ![]() | |
2023 | |
会议名称 | IEEE Conference on Computer Vision and Pattern Recognition |
会议日期 | 6.18-6.22 |
会议地点 | 加拿大温哥华市 |
摘要 | Recently, open-vocabulary learning has emerged to accomplish segmentation for arbitrary categories of text-based descriptions, which popularizes the segmentation system to more general-purpose application scenarios. However, existing methods devote to designing specialized architectures or parameters for specific segmentation tasks. These customized design paradigms lead to fragmentation between various segmentation tasks, thus hindering the uniformity of segmentation models. Hence in this paper, we propose FreeSeg, a generic framework to accomplish Unified, Universal and Open-Vocabulary Image Segmentation. FreeSeg optimizes an all-in-one network via one-shot training and employs the same architecture and parameters to handle diverse segmentation tasks seamlessly in the inference procedure. |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
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/173211/57147 |
专题 | 中国科学院工业视觉智能装备工程实验室_精密感知与控制 |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.ByteDance Inc 3.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Jie Qin,Jie Wu,Pengxiang Yan,et al. FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation[C],2023. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
FreeSeg_Unified Univ(5688KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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