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.

中文翻译：

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用于广义属性预测的师生网络半监督学习

本文提出了一种半监督学习的研究来解决视觉属性预测问题。在视觉算法的许多应用中，对物体视觉属性的精确识别很重要，但仍然具有挑战性。这是因为定义属性的类层次结构是不明确的，因此训练数据不可避免地受到类不平衡和标签稀疏的影响，从而导致缺乏有效的注释。一个直观的解决方案是找到一种方法，通过利用未标记的图像来有效地学习图像表示。考虑到这一点，我们提出了一种多教师单学生（MTSS）方法，其灵感来自多任务学习和半监督学习的提炼。我们的 MTSS 使用标签嵌入技术学习称为教师网络的特定任务领域专家，并通过强制模型模仿领域专家学习的分布来学习称为学生网络的统一模型。我们的实验表明，我们的方法不仅在时尚属性预测的各种基准上取得了有竞争力的性能，而且还提高了对未知领域的鲁棒性和跨领域适应性。