Image hashing is a principled approximate nearest neighbor approach to find
similar items to a query in a large collection of images. Hashing aims to learn
a binary-output function that maps an image to a binary vector. For optimal
retrieval performance, producing balanced hash codes with low-quantization
error to bridge the gap between the learning stage's continuous relaxation and
the inference stage's discrete quantization is important. However, in the
existing deep supervised hashing methods, coding balance and low-quantization
error are difficult to achieve and involve several losses. We argue that this
is because the existing quantization approaches in these methods are
heuristically constructed and not effective to achieve these objectives. This
paper considers an alternative approach to learning the quantization
constraints. The task of learning balanced codes with low quantization error is
re-formulated as matching the learned distribution of the continuous codes to a
pre-defined discrete, uniform distribution. This is equivalent to minimizing
the distance between two distributions. We then propose a computationally
efficient distributional distance by leveraging the discrete property of the
hash functions. This distributional distance is a valid distance and enjoys
lower time and sample complexities. The proposed single-loss quantization
objective can be integrated into any existing supervised hashing method to
improve code balance and quantization error. Experiments confirm that the
proposed approach substantially improves the performance of several
representative hashing~methods.

本文提出了一种替代学习量化限制的方法，将学习平衡码并降低量化误差的任务重新定义为将连续码的学习分布与预定义的离散均匀分布匹配，从而最小化两个分布之间的距离。同时，本文还提出了一种计算有效的分布距离，将其集成到现有的监督式哈希方法中能够提高代码平衡性和量化误差的性能表现。

量化的一种丧失：离散 Wasserstein 分布匹配的深层哈希

One Loss for Quantization: Deep Hashing with Discrete Wasserstein  Distributional Matching

Images tell powerful stories but cannot always be trusted. Matching images
back to trusted sources (attribution) enables users to make a more informed
judgment of the images they encounter online. We propose a robust image hashing
algorithm to perform such matching. Our hash is sensitive to manipulation of
subtle, salient visual details that can substantially change the story told by
an image. Yet the hash is invariant to benign transformations (changes in
quality, codecs, sizes, shapes, etc.) experienced by images during online
redistribution. Our key contribution is OSCAR-Net (Object-centric Scene Graph
Attention for Image Attribution Network); a robust image hashing model inspired
by recent successes of Transformers in the visual domain. OSCAR-Net constructs
a scene graph representation that attends to fine-grained changes of every
object's visual appearance and their spatial relationships. The network is
trained via contrastive learning on a dataset of original and manipulated
images yielding a state of the art image hash for content fingerprinting that
scales to millions of images.

本文提出了一种强大的图像哈希算法，名为 OSCAR-Net，它能对在线传播的图片进行匹配，并对精细细节进行注意力处理。通过对原始和修改图片的数据集进行对比学习训练，其得到的哈希值可用于内容指纹，适用于数百万张图片。