Feb 14, 2018 · This paper presents a deep compact code learning solution for efficient cross-modal similarity search. Many recent studies have proven that ...
Apr 16, 2019 · Our approach, dubbed shared predictive deep quantization (SPDQ), explicitly formulates a shared subspace across different modalities and two.
Quantization learns a shared lookup table (a dictionary) consisting of continuous values as well as binary codes for each data point to indicate which ...
A collective deep quantization (CDQ) approach, which is the first attempt to introduce quantization in end-to-end deep architecture for cross-modal ...
Oct 25, 2019 · ABSTRACT. The problem of cross-modal similarity search, which aims at making efficient and accurate queries across multiple domains, ...
Deep cross-modal hashing has become an essential tool for supervised multimodal search. These models tend to be optimized with large, curated multimodal ...
Shared Predictive Deep Quantization (SPDQ) [46] created a shared subspace across different modalities and private subspaces for individual modalities.
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Quantisation Models play a crucial role in nearest neighbor search by converting real-valued data into binary hashcodes, making it easier and faster to ...
In this paper, we propose a deep quantization approach, which is among the early attempts of leveraging deep neural networks into quantization-based cross-modal ...
Existing deep cross-modal hashing works uniformly build the relationships across different modalities by constructing a multi-layer neural network, and learn ...