Recent binary representation learning models usually require sophisticated
binary optimization, similarity measure or even generative models as
auxiliaries. However, one may wonder whether these non-trivial components are
needed to formulate practical and effective hashing models. In this paper, we
answer the above question by proposing an embarrassingly simple approach to
binary representation learning. With a simple classification objective, our
model only incorporates two additional fully-connected layers onto the top of
an arbitrary backbone network, whilst complying with the binary constraints
during training. The proposed model lower-bounds the Information Bottleneck
(IB) between data samples and their semantics, and can be related to many
recent `learning to hash' paradigms. We show that, when properly designed, even
such a simple network can generate effective binary codes, by fully exploring
data semantics without any held-out alternating updating steps or auxiliary
models. Experiments are conducted on conventional large-scale benchmarks, i.e.,
CIFAR-10, NUS-WIDE, and ImageNet, where the proposed simple model outperforms
the state-of-the-art methods.

本文提出一种简单的分类目标方法，仅在任意骨干网络的顶部附加两个全连接层，遵守训练期间的二进制约束条件，以此为基础流行实现二进制表示学习和哈希模型的任务，其可以有效地探索数据语义而不需要辅助模型，从而在 CIFAR-10、NUS-WIDE 和 ImageNet 的评测数据上优于现有技术。

尴尬地简单的二进制表示学习

Embarrassingly Simple Binary Representation Learning

In recent years, cross-modal retrieval has drawn much attention due to the
rapid growth of multimodal data. It takes one type of data as the query to
retrieve relevant data of another type. For example, a user can use a text to
retrieve relevant pictures or videos. Since the query and its retrieved results
can be of different modalities, how to measure the content similarity between
different modalities of data remains a challenge. Various methods have been
proposed to deal with such a problem. In this paper, we first review a number
of representative methods for cross-modal retrieval and classify them into two
main groups: 1) real-valued representation learning, and 2) binary
representation learning. Real-valued representation learning methods aim to
learn real-valued common representations for different modalities of data. To
speed up the cross-modal retrieval, a number of binary representation learning
methods are proposed to map different modalities of data into a common Hamming
space. Then, we introduce several multimodal datasets in the community, and
show the experimental results on two commonly used multimodal datasets. The
comparison reveals the characteristic of different kinds of cross-modal
retrieval methods, which is expected to benefit both practical applications and
future research. Finally, we discuss open problems and future research
directions.

本文对异构数据检索问题进行了探讨，综述并分类了已有的交叉检索方法，包括基于实值表示学习和基于二进制表示学习的方法，并介绍了多个常用的多模态数据集以及比较不同方法的实验结果。