A review of convolutional neural network architectures and their optimizations
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 …
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …
One loss for all: Deep hashing with a single cosine similarity based learning objective
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 …
binary hash codes discriminative and to minimize a quantization error. With further …
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 …
Exponential moving average normalization for self-supervised and semi-supervised learning
We present a plug-in replacement for batch normalization (BN) called exponential moving
average normalization (EMAN), which improves the performance of existing student-teacher …
average normalization (EMAN), which improves the performance of existing student-teacher …
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 …
Deep learning for approximate nearest neighbour search: A survey and future directions
Approximate nearest neighbour search (ANNS) in high-dimensional space is an essential
and fundamental operation in many applications from many domains such as multimedia …
and fundamental operation in many applications from many domains such as multimedia …
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 …
Efficient video transformers with spatial-temporal token selection
Video transformers have achieved impressive results on major video recognition
benchmarks, which however suffer from high computational cost. In this paper, we present …
benchmarks, which however suffer from high computational cost. In this paper, we present …
Semantic-aware adversarial training for reliable deep hashing retrieval
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 …
retrieval systems due to its efficiency and effectiveness. Recent studies have recognized that …
Badhash: Invisible backdoor attacks against deep hashing with clean label
Due to its powerful feature learning capability and high efficiency, deep hashing has
achieved great success in large-scale image retrieval. Meanwhile, extensive works have …
achieved great success in large-scale image retrieval. Meanwhile, extensive works have …