Predicting all applicable labels for a given image is known as multi-label
classification. Compared to the standard multi-class case (where each image has
only one label), it is considerably more challenging to annotate training data
for multi-label classification. When the number of potential labels is large,
human annotators find it difficult to mention all applicable labels for each
training image. Furthermore, in some settings detection is intrinsically
difficult e.g. finding small object instances in high resolution images. As a
result, multi-label training data is often plagued by false negatives. We
consider the hardest version of this problem, where annotators provide only one
relevant label for each image. As a result, training sets will have only one
positive label per image and no confirmed negatives. We explore this special
case of learning from missing labels across four different multi-label image
classification datasets for both linear classifiers and end-to-end fine-tuned
deep networks. We extend existing multi-label losses to this setting and
propose novel variants that constrain the number of expected positive labels
during training. Surprisingly, we show that in some cases it is possible to
approach the performance of fully labeled classifiers despite training with
significantly fewer confirmed labels.

研究一种新的多标签图像分类问题，针对训练数据需要手动标注出所有适用标签的挑战，探索使用缺失标签进行学习的特殊情况，并尝试通过线性分类器和端到端微调深度网络提出新的损失函数来解决这个问题。结果表明，在一些情况下，使用更少的标签进行训练可以接近全部标签训练的性能水平。