全频谱提示调整与稀疏的MoE,用于开放式识别.
Yifei Xie1, Chuanxing Geng2, Yahao Hu1
1Command and Control Engineering College, Army Engineering University, Nanjing, 210007, Jiangsu, China.
概括
这项研究引入了全频谱快速调整与稀疏混合专家 (FSMoE) 的开放式识别. FSMoE通过将低级别的视觉特征集成到文本提示中来增强视觉语言模型,从而提高了未知的类的识别.
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:
- 开放式识别 (OSR) 的进步通常集中在视觉语言模型 (VLM) 的高级视觉特征上.
- 来自浅层图像编码器层的低级视觉细节在当前的OSR方法中未得到充分利用.
- 将低级别的特征集成到高级别的文本提示中,带来了表示挑战.
研究的目的:
- 提出一种新的方法,即全频谱快速调整与稀少的专家组合 (FSMoE),以提高开放集的识别.
- 为了利用VLM图像编码器层中的全频谱视觉功能,以改进文本提示生成.
- 为应对将低级别视觉细节集成到提示中以更好地识别未知类的挑战.
主要方法:
- FSMoE利用VLM的全频谱视觉特征来增强文本提示.
- 两组文本令牌 (高级和低级) 与相应的视觉特征相互作用.
- 一个稀疏的专家混合机制自适应地选择和权衡低级别的视觉特征.
- 路由一致性对比性损失在专家之间强制执行类内一致性.
主要成果:
- 拟议的FSMoE方法有效地增强了使用高层和低层视觉特征的文字提示.
- 稀疏的专家混合机制成功地减轻了低级别视觉细节中的冗余性.
- 实验结果验证了FSMoE在开放式识别任务中的有效性.
结论:
- FSMoE通过将全谱视觉信息集成到文本提示中,为开放集的识别提供了一个全面的方法.
- 该方法克服了仅依赖高级特征的局限性,并解决了特征表示差异.
- FSMoE显示出在使用视觉语言模型推进开放集识别领域的巨大潜力.
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