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语言感知视觉转换器用于引用细分
IEEE transactions on pattern analysis and machine intelligence
|September 25, 2024
概括
本研究介绍了语言意识视觉转换器 (LAVT) 用于引用细分,通过在模型早期融合语言和视觉特征来实现更好的对象本地化. LAVT 提高了图像和视频的分段精度.
科学领域:
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 引用细分对于视觉语言任务至关重要,需要基于文本描述的精确对象本地化.
- 现有的方法通常依赖于跨模态解码器中的晚期融合,这可能会限制对齐准确度.
- 转换器已经在视觉语言任务中取得了成功,但它们在引用细分中的应用可以得到改进.
研究的目的:
- 提出一种新的框架,语言意识视觉转换器 (LAVT),用于改进引用细分.
- 通过在视觉转换器编码器的早期融合语言和视觉特征来增强交叉模式对齐.
- 开发一个统一的框架,能够处理图像和视频引用分段任务.
主要方法:
- 在视觉转换器编码器的中间层中实现了语言和视觉特征的早期融合.
- 引入了一种密集的注意力机制,用于捕获像素特定的语言线索.
- 开发了密集注意力机制的3D版本,用于视频细分的多尺度卷积运算符,利用时空依赖.
- 为图像和视频参考细分提出了一个统一的LAVT框架.
主要成果:
- 与以前的方法相比,实现了明显更好的跨模式对齐.
- 在七个基准数据集上展示了最先进的性能,用于引用图像和视频细分.
- 拟议的LAVT框架通过轻量化口罩预测器提供了准确的细分.
结论:
- 视觉转换器编码器中的多模特特征的早期融合是引用细分的有效策略.
- 该LAVT框架为图像和视频参考细分提供了统一和高效的方法.
- 提出的密集注意力机制成功地提取了像素特定的语言线索,提高了细分的准确性.
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