SKDF:一个简单的知识蒸框架,用于将开放词汇知识蒸到开放世界的对象探测器
IEEE transactions on pattern analysis and machine intelligence
|August 19, 2025
概括
本研究引入了开放世界对象检测 (OWOD) 的新方法,使模型能够在没有文本提示的情况下检测已知和未知对象. 该方法改进了已知的对象学习,同时检测了新项目,创建了一个更强大的系统.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 开放世界对象检测 (OWOD) 扩展了经典的对象检测,要求模型识别已知和未知对象.
- 视觉语言模型 (VLM) 拥有广泛的开放世界的知识,但受限于本地化文本提示.
- 现实世界的场景往往缺乏预先定义的语言描述,阻碍了OWOD中的VLM应用.
研究的目的:
- 通过将他们的知识蒸成一种语言无关的探测器,使大型预训练的VLM适应OWOD任务.
- 解决未知物体检测的挑战,而不依赖于文本提示.
- 提高OWOD系统在各种现实环境中的性能和稳定性.
主要方法:
- 从VLM到语言无关的检测器的知识蒸.
- 一种减重训练策略,以减轻对已知对象学习的负面影响.
- 级联解器解器以分离本地化和识别学习过程.
- 开发用于评估未知物体检测的"IntensiveSet"基准.
主要成果:
- 简单的知识蒸惊人地改善了未知物体检测,即使数据有限.
- 拟议的降重训练和级联解器有效地保留了已知的对象学习.
- 实验证明了在OWOD,MS-COCO和新的IntensiveSet基准上提出的方法的有效性.
结论:
- 提出的方法成功地将OWOD的开放世界知识提炼出来,同时保留已知的对象识别.
- 这些技术增强了探测器在复杂环境中处理可见和不可见的物体的能力.
- IntensiveSet基准为推进OWOD研究提供了宝贵的资源,特别是在未知物体检测方面.
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