We present a novel language-driven ordering alignment method for ordinal
classification. The labels in ordinal classification contain additional
ordering relations, making them prone to overfitting when relying solely on
training data. Recent developments in pre-trained vision-language models
inspire us to leverage the rich ordinal priors in human language by converting
the original task into a vision-language alignment task. Consequently, we
propose L2RCLIP, which fully utilizes the language priors from two
perspectives. First, we introduce a complementary prompt tuning technique
called RankFormer, designed to enhance the ordering relation of original rank
prompts. It employs token-level attention with residual-style prompt blending
in the word embedding space. Second, to further incorporate language priors, we
revisit the approximate bound optimization of vanilla cross-entropy loss and
restructure it within the cross-modal embedding space. Consequently, we propose
a cross-modal ordinal pairwise loss to refine the CLIP feature space, where
texts and images maintain both semantic alignment and ordering alignment.
Extensive experiments on three ordinal classification tasks, including facial
age estimation, historical color image (HCI) classification, and aesthetic
assessment demonstrate its promising performance.

本文提出了一种基于自然语言处理的序列分类方法，包括相应的损失函数，并通过实验证明在面部年龄估计、历史彩色图像分类和审美评估等任务中表现良好