PEEB：具备可解释可编辑语言瓶颈的基于部件的图像分类器
PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck
AI总结：
针对基于CLIP的分类器在细粒度零样本场景下表现不佳的问题，提出可解释可编辑的部件级分类器PEEB，通过文本描述符匹配部件嵌入实现分类，在零样本和监督设置下均达SOTA，且支持无需重训练的分类器编辑。
摘要：
基于CLIP的分类器依赖包含文本编码器已知{类别名称}的提示词，因此在新类别或名称极少出现在互联网上的类别（如鸟类的学名）上表现不佳。针对细粒度分类任务，我们提出PEEB——一种可解释且可编辑的分类器，其功能包括：（1）将类别名称转化为一组描述该类别视觉部件的文本描述符；（2）将检测到的部件的嵌入与每个类别对应的文本描述符进行匹配，计算用于分类的logit分数。在类别名称未知的零样本设置下，PEEB的性能大幅超越CLIP（top-1准确率提升约10倍）。与基于部件的分类器相比，PEEB不仅在监督学习设置下达到当前最优（SOTA）水平，在CUB-200和Dogs-120数据集上的准确率分别为88.80%和92.20%，还是首个支持用户通过编辑文本描述符构建新分类器且无需任何重新训练的模型。与概念瓶颈模型相比，PEEB在零样本和监督学习两种设置下也均达到SOTA水平。
英文摘要：
CLIP-based classifiers rely on the prompt containing a {class name} that is known to the text encoder. Therefore, they perform poorly on new classes or the classes whose names rarely appear on the Internet (e.g., scientific names of birds). For fine-grained classification, we propose PEEB - an explainable and editable classifier to (1) express the class name into a set of text descriptors that describe the visual parts of that class; and (2) match the embeddings of the detected parts to their textual descriptors in each class to compute a logit score for classification. In a zero-shot setting where the class names are unknown, PEEB outperforms CLIP by a huge margin (~10x in top-1 accuracy). Compared to part-based classifiers, PEEB is not only the state-of-the-art (SOTA) on the supervised-learning setting (88.80% and 92.20% accuracy on CUB-200 and Dogs-120, respectively) but also the first to enable users to edit the text descriptors to form a new classifier without any re-training. Compared to concept bottleneck models, PEEB is also the SOTA in both zero-shot and supervised-learning settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
