Oct 17, 2021 · In this paper, we propose to use Graph Convolutional Networks (GCNs) to exploit the local structure information of datasets for cross-modal hash learning.
Oct 24, 2021 · Cross-modal hashing aims to map the data of different modalities into a common binary space to accelerate the retrieval speed. Re- cently, deep ...
This repository contains the code of Local Graph Convolutional Networks Hashing (LGCNH) accepted at MM'21. Part of the code is modified from DCMH.
In this paper, we propose a Graph Convolutional Hashing (GCH) approach, which learns modality-unified binary codes via an affinity graph.
Jan 6, 2022 · This paper focuses on the research of graph convolutional networks for cross-modal information retrieval and has a general understanding of cross-modal ...
May 23, 2024 · In this paper, we propose an innovative approach called the Joint-Modal Graph Convolutional Hashing (JMGCH) method via adaptive weight assignment for ...
In this paper, we propose a Graph Convolutional Hashing (GCH) approach, which learns modality-unified binary codes via an affinity graph. An end-to-end deep ...
This library is an open-source repository that contains cross-modal retrieval methods and codes. 2. Supported Methods
This paper presents a novel deep hashing approach, called Proxy-based Graph Convolutional Hashing (PGCH), for cross-modal retrieval.
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Dec 2, 2022 · In this paper, we propose a novel SCH ap- proach named Modality-specific and Cross-modal Graph Convolutional Networks (MCGCN). The network.