Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, given the fact that it could be expensive in practice to annotate all labels in every training image. Existing works on partial-label learning focus on the case where each training image is annotated with only a subset of its labels. A special case is to annotate only one positive label in each training image. To further relieve the annotation burden and enhance the performance of the classifier, this paper proposes a new partial-label setting in which only a subset of the training images are labeled, each with only one positive label, while the rest of the training images remain unlabeled. To handle this new setting, we propose an end-to-end deep network, PLMCL (Partial Label Momentum Curriculum Learning), that can learn to produce confident pseudo labels for both partially-labeled and unlabeled training images. The novel momentum-based law updates soft pseudo labels on each training image with the consideration of the updating velocity of pseudo labels, which help avoid trapping to low-confidence local minimum, especially at the early stage of training in lack of both observed labels and confidence on pseudo labels. In addition, we present a confidence-aware scheduler to adaptively perform easy-to-hard learning for different labels. Extensive experiments demonstrate that our proposed PLMCL outperforms many state-of-the-art multi-label classification methods under various partial-label settings on three different datasets.
翻译:多标签图像分类的目的是在图像中预测所有可能的标签。 通常, 多标签图像分类是作为一个部分标签学习问题来设定的。 通常, 它是一个部分标签学习问题, 因为在实际中, 批注每个培训图像中的所有标签可能花费很多, 而其他培训图像则没有标记。 部分标签学习的当前工作侧重于每个培训图像加注时只加一个标签的个案。 一个特例是在每个培训图像中只加注一个肯定标签。 为了进一步减轻批注负担, 提高分类员的性能, 本文建议一个新的部分标签设置, 只有一组部分培训图像加标签, 而每个部分只加一个正标签, 而其余的培训图像则不加标签。 为了处理这个新设置, 我们提议了一个端到端到端的深网络, PLMMCL( 部分标签课程学习), 可以学习为部分标签和未加标签的图像制作一个自信的假标签。 新的动力法更新了每部培训的软化的多标签, 并且考虑更新了假标签的升级速度, 有助于避免隐藏到低信任度的局部标签, 最低程度的标签,, 缺乏我们观察到最容易的标签的标签。 演示阶段 。 在不同的标签上, 演示阶段里, 缺少一个不同的标签上, 演示中, 演示中, 演示到目前不同的标签, 演示到不同的标签, 演示到不同的标签。