A deep hashing model typically has two main learning objectives: to make the learned
binary hash codes discriminative and to minimize a quantization error. With further …

Nearest neighbor search aims at obtaining the samples in the database with the smallest
distances from them to the queries, which is a basic task in a range of fields, including …

Deep hashing is an appealing approach for large-scale image retrieval. Most existing
supervised deep hashing methods learn hash functions using pairwise or triple image …

Deep image hashing aims to map an input image to compact binary codes by deep neural
network, to enable efficient image retrieval across large-scale dataset. Due to the explosive …

Recently, Transformer has emerged as a new architecture in deep learning by utilizing self-
attention without convolution. Transformer is also extended to Vision Transformer (ViT) for …

In hash-based image retrieval systems, degraded or transformed inputs usually generate
different codes from the original, deteriorating the retrieval accuracy. To mitigate this issue …

Deep hashing has been intensively studied and successfully applied in large-scale image
retrieval systems due to its efficiency and effectiveness. Recent studies have recognized that …

The recently developed vision transformer (ViT) has achieved promising results on image
retrieval compared to convolutional neural networks. However, most of these vision …

This chapter investigates capabilities of Privacy-Preserving Deep Learning (PPDL)
mechanisms against various forms of privacy attacks. First, we propose to quantitatively …

Transformer based on self-attention mechanism has made remarkable achievements in
natural language processing, which inspired the application research of Transformer in …