This paper presents a study on semi-supervised learning to solve the visual
attribute prediction problem. In many applications of vision algorithms, the
precise recognition of visual attributes of objects is important but still
challenging. This is because defining a class hierarchy of attributes is
ambiguous, so training data inevitably suffer from class imbalance and label
sparsity, leading to a lack of effective annotations. An intuitive solution is
to find a method to effectively learn image representations by utilizing
unlabeled images. With that in mind, we propose a multi-teacher-single-student
(MTSS) approach inspired by the multi-task learning and the distillation of
semi-supervised learning. Our MTSS learns task-specific domain experts called
teacher networks using the label embedding technique and learns a unified model
called a student network by forcing a model to mimic the distributions learned
by domain experts. Our experiments demonstrate that our method not only
achieves competitive performance on various benchmarks for fashion attribute
prediction, but also improves robustness and cross-domain adaptability for
unseen domains.

本文提出了一种多教师 - 单学生 (MTSS) 方法，通过使用标签嵌入技术，可以学习任务特定的领域专家，并通过迫使模型模仿领域专家所学习的分布方法，学习图像表示的有效方法，从而实现了对视觉属性预测问题的半监督学习。实验结果表明，该方法不仅在时尚属性预测的各种基准测试上取得了竞争性的性能，还提高了不可见领域的鲁棒性和跨域适应性。