Long-tailed multi-label visual recognition (LTML) task is a highly challenging task due to the label co-occurrence and imbalanced data distribution. In this work, we propose a unified framework for LTML, namely prompt tuning with class-specific embedding loss (LMPT), capturing the semantic feature interactions between categories by combining text and image modality data and improving the performance synchronously on both head and tail classes. Specifically, LMPT introduces the embedding loss function with class-aware soft margin and re-weighting to learn class-specific contexts with the benefit of textual descriptions (captions), which could help establish semantic relationships between classes, especially between the head and tail classes. Furthermore, taking into account the class imbalance, the distribution-balanced loss is adopted as the classification loss function to further improve the performance on the tail classes without compromising head classes. Extensive experiments are conducted on VOC-LT and COCO-LT datasets, which demonstrates that the proposed method significantly surpasses the previous state-of-the-art methods and zero-shot CLIP in LTML. Our codes are fully available at \url{https://github.com/richard-peng-xia/LMPT}.

本研究提出了一种统一框架——基于类特定嵌入损失的提示调整（LMPT）——用于解决多标记视觉识别中长尾类别的问题，结合文本和图像，引入嵌入损失函数，以类感知的软间距和重新权重为基础，学习特定于类别的上下文，采用分布平衡型损失函数作为分类损失函数，进一步提高了小类别的性能，与前人研究方法和零样本CLIP相比，结果表明所提出的方法显著优于先前的最先进方法。

LMPT：长尾多标签视觉识别中的类别特定嵌入损失的提示调整