Visual-semantic embedding models have been recently proposed and shown to be
effective for image classification and zero-shot learning, by mapping images
into a continuous semantic label space. Although several approaches have been
proposed for single-label embedding tasks, handling images with multiple labels
(which is a more general setting) still remains an open problem, mainly due to
the complex underlying corresponding relationship between image and its labels.
In this work, we present Multi-Instance visual-semantic Embedding model (MIE)
for embedding images associated with either single or multiple labels. Our
model discovers and maps semantically-meaningful image subregions to their
corresponding labels. And we demonstrate the superiority of our method over the
state-of-the-art on two tasks, including multi-label image annotation and
zero-shot learning.

通过 Multi-Instance visual-semantic Embedding model (MIE) 模型，我们可以发现和映射成对应标签的语义意义的图像子区域，有效提升了多标签图像注释和零样本学习任务的性能。

多实例视觉语义嵌入

Multi-Instance Visual-Semantic Embedding

Many visual recognition problems can be approached by counting instances. To
determine whether an event is present in a long internet video, one could count
how many frames seem to contain the activity. Classifying the activity of a
group of people can be done by counting the actions of individual people.
Encoding these cardinality relationships can reduce sensitivity to clutter, in
the form of irrelevant frames or individuals not involved in a group activity.
Learned parameters can encode how many instances tend to occur in a class of
interest. To this end, this paper develops a powerful and flexible framework to
infer any cardinality relation between latent labels in a multi-instance model.
Hard or soft cardinality relations can be encoded to tackle diverse levels of
ambiguity. Experiments on tasks such as human activity recognition, video event
detection, and video summarization demonstrate the effectiveness of using
cardinality relations for improving recognition results.

本文基于多实例模型与基数关系建立了一个灵活而强大的框架，通过将基数关系编码到模型中，以改善视觉识别任务效果，如人类活动识别、视频事件检测等。