The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

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 …

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 …

We present a plug-in replacement for batch normalization (BN) called exponential moving
average normalization (EMAN), which improves the performance of existing student-teacher …

Supervised deep learning-based hash and vector quantization are enabling fast and large-
scale image retrieval systems. By fully exploiting label annotations, they are achieving …

Approximate nearest neighbour search (ANNS) in high-dimensional space is an essential
and fundamental operation in many applications from many domains such as multimedia …

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 …

Video transformers have achieved impressive results on major video recognition
benchmarks, which however suffer from high computational cost. In this paper, we present …

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 …

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 …