Hypernymy, textual entailment, and image captioning can be seen as special
cases of a single visual-semantic hierarchy over words, sentences, and images.
In this paper we advocate for explicitly modeling the partial order structure
of this hierarchy. Towards this goal, we introduce a general method for
learning ordered representations, and show how it can be applied to a variety
of tasks involving images and language. We show that the resulting
representations improve performance over current approaches for hypernym
prediction and image-caption retrieval.

本文倡导明确模拟视觉 - 语义层次中的偏序结构，介绍一个学习有序表示的通用方法，并展示了如何将其应用于涉及图像和语言的各种任务中，特别是超义预测和图像字幕检索，结果表明得到的表示优于当前方法。

图像和语言的序列嵌入

Order-Embeddings of Images and Language

In this paper, we study ordered representations of data in which different
dimensions have different degrees of importance. To learn these representations
we introduce nested dropout, a procedure for stochastically removing coherent
nested sets of hidden units in a neural network. We first present a sequence of
theoretical results in the simple case of a semi-linear autoencoder. We
rigorously show that the application of nested dropout enforces identifiability
of the units, which leads to an exact equivalence with PCA. We then extend the
algorithm to deep models and demonstrate the relevance of ordered
representations to a number of applications. Specifically, we use the ordered
property of the learned codes to construct hash-based data structures that
permit very fast retrieval, achieving retrieval in time logarithmic in the
database size and independent of the dimensionality of the representation. This
allows codes that are hundreds of times longer than currently feasible for
retrieval. We therefore avoid the diminished quality associated with short
codes, while still performing retrieval that is competitive in speed with
existing methods. We also show that ordered representations are a promising way
to learn adaptive compression for efficient online data reconstruction.

通过引入嵌套 dropout，我们在神经网络中学习有序的数据表示，并构建了基于哈希的数据结构，实现了时间复杂度对数级和与表示维度无关的检索，同时展示了有序表示是一种学习有效在线数据重构的有前途的方法。