简单的原始体与可行性和上下文依赖性为开放世界的构成式零射击学习
IEEE transactions on pattern analysis and machine intelligence
|October 9, 2023
概括
本研究介绍了一种新的开放世界构成式零拍摄学习 (OW-CZSL) 方法,以提高新型图像构成的识别能力. 该方法模拟了可行性和上下文性,在基准数据集上表现优于现有的方法.
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:
- 开放式世界构成式零射击学习 (OW-CZSL) 面临着由于大枢纽性而带来新的状态对象构成的挑战.
- 传统的方法往往忽略了状态,物体及其组成之间的关键依赖关系.
- 现有的方法独立处理简单的原始体,导致显著的性能降低.
研究的目的:
- 通过建模可行性和上下文依赖性来解决当前OW-CZSL方法的局限性.
- 提高在图像中的新状态对象组合的识别.
- 开发一个强大的框架来处理构成式学习的固有复杂性.
主要方法:
- 建模取决于可行性 (例如",毛的"与"猫"相比"建筑") 和上下文性 (例如",猫"干湿).
- 设计语义注意力 (SA) 来捕捉可行性语义并减少不可能的预测.
- 提出生成的知识解 (KD),以创建无偏见的图像表示和减轻上下文偏见.
- 将学习的可行性和上下文性集成到一个独立的组成概率模型中.
主要成果:
- 拟议的SA和KD引导的简单原始体 (SAD-SP) 方法显示出优越或竞争性性能.
- 有效地捕捉和利用可行性和上下文性,以改善作文理解.
- 在OW-CZSL的三个基准数据集上取得了最先进的结果.
结论:
- 开发的SAD-SP方法有效地模拟了OW-CZSL中的复杂依赖关系.
- 语义注意力和知识解是增强构成学习的关键组成部分.
- 该方法在识别不同图像环境中的新状态对象组合方面取得了重大进展.
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