自行校准的CLIP用于免费培训的开放词汇细分
概括
自校准的CLIP (SC-CLIP) 通过解决异常令牌来增强视觉语言模型的开放词汇细分. 这种无需培训的方法可以改善局部细节的捕获,并显著提高性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 预先训练有素的视觉语言模型,如CLIP,使开放的词汇细分能够具有强大的零射击能力.
- 基于视觉变压器 (ViT) 的CLIP模型因全球特征交互和异常令牌而在细分中扎着局部细节.
- 基于ViT的CLIP中的异常令牌降低了空间意识,并阻碍了细分性能.
研究的目的:
- 提出一种没有培训的方法,即自校准CLIP (SC-CLIP),以提高CLIP对细分任务的性能.
- 解决异常令牌的问题,并提高视觉语言模型中局部细节的捕获.
- 在不引入新参数或额外的骨干的情况下,校准CLIP以获得更精细的表示.
主要方法:
- SC-CLIP根据当地环境识别和替换异常令牌.
- 它通过增强特征可区分性和注意力相关性来减少异常令牌的影响.
- 双通道策略集成了多层次的功能,以免训练的方式丰富当地细节.
主要成果:
- 在所有测试的数据集中,SC-CLIP实现了最先进的结果,超过了以前的方法9.5%.
- 这种方法显著提高了香草CLIP ViT-L/14的性能6.8倍.
- SC-CLIP增强了CLIP的特征表示,提高了细节性和语义连贯性.
结论:
- 通过解决异常令牌和增强本地细节,SC-CLIP有效校准CLIP以提高细分性能.
- 没有培训的方法保留了CLIP的概括能力,同时显著提高了性能.
- SC-CLIP显示了其对其他视觉语言模型和任务的广泛应用.
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