互因果语义蒸馏网络用于零样本学习
Mutually Causal Semantic Distillation Network for Zero-Shot Learning
AI总结：
本文提出互因果语义蒸馏网络MSDN++，通过双向因果注意力子网络学习视觉与属性间的内在语义表示，提升零样本学习性能。
摘要：
零样本学习（ZSL）旨在利用侧信息（如属性）识别未见类别。其关键任务是如何在已见类别上推断视觉与属性特征间的潜在语义知识，从而实现有效的语义知识迁移。先前工作仅利用弱监督方式学习单向注意力，导致语义表示有限。本文提出MSDN++，包含属性→视觉因果注意力子网络和视觉→属性因果注意力子网络，通过语义蒸馏损失指导两子网络协作学习，提升零样本学习性能。
英文摘要：
Zero-shot learning (ZSL) aims to recognize the unseen classes in the open-world guided by the side-information (e.g., attributes). Its key task is how to infer the latent semantic knowledge between visual and attribute features on seen classes, and thus conducting a desirable semantic knowledge transfer from seen classes to unseen ones. Prior works simply utilize unidirectional attention within a weakly-supervised manner to learn the spurious and limited latent semantic representations, which fail to effectively discover the intrinsic semantic knowledge (e.g., attribute semantic) between visual and attribute features. To solve the above challenges, we propose a mutually causal semantic distillation network (termed MSDN++) to distill the intrinsic and sufficient semantic representations for ZSL. MSDN++ consists of an attribute$\rightarrow$visual causal attention sub-net that learns attribute-based visual features, and a visual$\rightarrow$attribute causal attention sub-net that learns visual-based attribute features. The causal attentions encourages the two sub-nets to learn causal vision-attribute associations for representing reliable features with causal visual/attribute learning. With the guidance of semantic distillation loss, the two mutual attention sub-nets learn collaboratively and teach each other throughout the training process. Extensive experiments on three widely-used benchmark datasets (e.g., CUB, SUN, AWA2, and FLO) show that our MSDN++ yields significant improvements over the strong baselines, leading to new state-of-the-art performances.
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。
