Hash coding has been widely used in the approximate nearest neighbor search
for large-scale image retrieval. Recently, many deep hashing methods have been
proposed and shown largely improved performance over traditional
feature-learning-based methods. Most of these methods examine the pairwise
similarity on the semantic-level labels, where the pairwise similarity is
generally defined in a hard-assignment way. That is, the pairwise similarity is
'1' if they share no less than one class label and '0' if they do not share
any. However, such similarity definition cannot reflect the similarity ranking
for pairwise images that hold multiple labels. In this paper, a new deep
hashing method is proposed for multi-label image retrieval by re-defining the
pairwise similarity into an instance similarity, where the instance similarity
is quantified into a percentage based on the normalized semantic labels. Based
on the instance similarity, a weighted cross-entropy loss and a minimum mean
square error loss are tailored for loss-function construction, and are
efficiently used for simultaneous feature learning and hash coding. Experiments
on three popular datasets demonstrate that, the proposed method outperforms the
competing methods and achieves the state-of-the-art performance in multi-label
image retrieval.

本文提出了一种新的深度哈希方法，通过将成对相似性重新定义为实例相似性，基于实例相似性，构造了加权交叉熵和最小均方误差损失，用于同时学习特征和哈希编码，实验表明，这种方法优于竞争方法，在多标签图像检索中实现了最先进的性能。

使用软对比相似度提高多标签图像检索的深度哈希

Improved Deep Hashing with Soft Pairwise Similarity for Multi-label  Image Retrieval

Hashing is widely applied to approximate nearest neighbor search for
large-scale multimodal retrieval with storage and computation efficiency.
Cross-modal hashing improves the quality of hash coding by exploiting semantic
correlations across different modalities. Existing cross-modal hashing methods
first transform data into low-dimensional feature vectors, and then generate
binary codes by another separate quantization step. However, suboptimal hash
codes may be generated since the quantization error is not explicitly minimized
and the feature representation is not jointly optimized with the binary codes.
This paper presents a Correlation Hashing Network (CHN) approach to cross-modal
hashing, which jointly learns good data representation tailored to hash coding
and formally controls the quantization error. The proposed CHN is a hybrid deep
architecture that constitutes a convolutional neural network for learning good
image representations, a multilayer perception for learning good text
representations, two hashing layers for generating compact binary codes, and a
structured max-margin loss that integrates all things together to enable
learning similarity-preserving and high-quality hash codes. Extensive empirical
study shows that CHN yields state of the art cross-modal retrieval performance
on standard benchmarks.

本论文提出了一种新的跨模式哈希方法，通过联合学习数据特征和量化误差控制实现高质量哈希编码，并在标准测试中表现良好。