A review on generative adversarial networks: Algorithms, theory, and applications
Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …
however, they have been studied since 2014, and a large number of algorithms have been …
A decade survey of content based image retrieval using deep learning
SR Dubey - IEEE Transactions on Circuits and Systems for …, 2021 - ieeexplore.ieee.org
The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …
against a query image. Generally, the similarity between the representative features of the …
Survey on deep multi-modal data analytics: Collaboration, rivalry, and fusion
Y Wang - ACM Transactions on Multimedia Computing …, 2021 - dl.acm.org
With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …
major stream for big data, where each modal/view encodes individual property of data …
Weakly-supervised semantic guided hashing for social image retrieval
Hashing has been widely investigated for large-scale image retrieval due to its search
effectiveness and computation efficiency. In this work, we propose a novel Semantic Guided …
effectiveness and computation efficiency. In this work, we propose a novel Semantic Guided …
One loss for quantization: Deep hashing with discrete wasserstein distributional matching
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 …
to a query in a large collection of images. Hashing aims to learn a binary-output function that …
Self-supervised product quantization for deep unsupervised image retrieval
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 …
scale image retrieval systems. By fully exploiting label annotations, they are achieving …
Auto-encoding twin-bottleneck hashing
Conventional unsupervised hashing methods usually take advantage of similarity graphs,
which are either pre-computed in the high-dimensional space or obtained from random …
which are either pre-computed in the high-dimensional space or obtained from random …
Distillhash: Unsupervised deep hashing by distilling data pairs
Due to storage and search efficiency, hashing has become significantly prevalent for nearest
neighbor search. Particularly, deep hashing methods have greatly improved the search …
neighbor search. Particularly, deep hashing methods have greatly improved the search …
A survey on deep hashing methods
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 …
distances from them to the queries, which is a basic task in a range of fields, including …
Deep unsupervised image hashing by maximizing bit entropy
Y Li, J van Gemert - Proceedings of the AAAI Conference on Artificial …, 2021 - ojs.aaai.org
Unsupervised hashing is important for indexing huge image or video collections without
having expensive annotations available. Hashing aims to learn short binary codes for …
having expensive annotations available. Hashing aims to learn short binary codes for …