Zero-shot learning (ZSL) models rely on learning a joint embedding space
where both textual/semantic description of object classes and visual
representation of object images can be projected to for nearest neighbour
search. Despite the success of deep neural networks that learn an end-to-end
model between text and images in other vision problems such as image
captioning, very few deep ZSL model exists and they show little advantage over
ZSL models that utilise deep feature representations but do not learn an
end-to-end embedding. In this paper we argue that the key to make deep ZSL
models succeed is to choose the right embedding space. Instead of embedding
into a semantic space or an intermediate space, we propose to use the visual
space as the embedding space. This is because that in this space, the
subsequent nearest neighbour search would suffer much less from the hubness
problem and thus become more effective. This model design also provides a
natural mechanism for multiple semantic modalities (e.g., attributes and
sentence descriptions) to be fused and optimised jointly in an end-to-end
manner. Extensive experiments on four benchmarks show that our model
significantly outperforms the existing models. Code is available at
this https URL

本研究提出使用视觉空间作为嵌入空间，以解决深度零样本学习模型的瓶颈问题，并提供了一种自然的机制来联合优化多个语义模态。实验表明，与现有模型相比，该模型明显优于其他模型。

零样本学习的深度嵌入模型学习

Learning a Deep Embedding Model for Zero-Shot Learning

Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.

本文提出了一种深度学习结构的监督哈希方法，其中图像通过经过精心设计的卷积神经网络映射为二进制代码，通过三个构建模块的流水线过程（卷积子网络，划分编码模块和三元组排序损失），实现最近邻搜索的高效率和精确度。