Despite significant progress in object categorization, in recent years, a
number of important challenges remain; mainly, the ability to learn from
limited labeled data and to recognize object classes within large, potentially
open, set of labels. Zero-shot learning is one way of addressing these
challenges, but it has only been shown to work with limited sized class
vocabularies and typically requires separation between supervised and
unsupervised classes, allowing former to inform the latter but not vice versa.
We propose the notion of vocabulary-informed learning to alleviate the above
mentioned challenges and address problems of supervised, zero-shot, generalized
zero-shot and open set recognition using a unified framework. Specifically, we
propose a weighted maximum margin framework for semantic manifold-based
recognition that incorporates distance constraints from (both supervised and
unsupervised) vocabulary atoms. Distance constraints ensure that labeled
samples are projected closer to their correct prototypes, in the embedding
space, than to others. We illustrate that resulting model shows improvements in
supervised, zero-shot, generalized zero-shot, and large open set recognition,
with up to 310K class vocabulary on Animal with Attributes and ImageNet
datasets.

该研究提出了一种基于语义流形的加权最大间隔框架解决零样本学习中面临的挑战，包括有限标记数据、大量标签分类和开放集分类。在 Animal with Attributes 和 ImageNet 数据集上，模型表现得到了显著提高，类词汇量最高可达 310K。