Cross-modal hashing has demonstrated advantages on fast retrieval tasks. It improves the quality of hash coding by exploiting semantic correlation across different modalities. In supervised cross-modal hashing, the learning of hash function replies on the quality of extracted features, for which deep ...
To take full advantage of heterogeneous correlation, many deep cross-modal retrieval methods have been proposed in recent years, such as references33,34,35. For instance, deep discrete cross-modal hashing with multiple supervision method34 designs a semantic network to fully exploit the semantic ...
Learning Cross-modal Embeddings for Cooking Recipes and Food Images )+dataset 输出:sentence(image) rank list method 文章的framework如下所示。 主要是将文本和图像映射到共享的子空间,然后在子空间上做cosine...这是CVPR2017的一篇做cross-modalretrieval的文章,paper和相关数据代码链接http://im2recipe.csail....
Deep consistency-preserving hash auto-encoders for neuroimage cross-modal retrieval Article Open access 09 February 2023 Semantic embedding based online cross-modal hashing method Article Open access 06 January 2024 A tied-weight autoencoder for the linear dimensionality reduction of sample data ...
The existing cross-modal hashing methods mainly includes unsupervised methods and supervised methods. The unsupervised methods learn the common Hamming space by exploring similarity among modalities data, such as Unsupervised Deep Cross-modality Spectral Hashing (DCSH) [13], Unsupervised Knowledge ...
Hashing methods have been proposed for the cross-modal retrieval tasks due to their flexibility and effectiveness. The main idea of cross-modal hashing is to embed heterogeneous multimedia data into common Hamming space. How to effectively exploit the modal semantic information and reduce optimization ...
Deep Semantic Hashing with Generative Adversarial Networks 论文review(翻译),程序员大本营,技术文章内容聚合第一站。
* 题目: AlertTrap: A study on object detection in remote insects trap monitoring system using on-the-edge deep learning platform* 链接: arxiv.org/abs/2112.1334* 作者: An D. Le,Duy A. Pham,Dong T. Pham,Hien B. Vo* 摘要: 果蝇是对水果产量最有害的昆虫之一。在 AlertTrap 中,使用不同...
Source code for TCSVT paper “Deep Semantic-Aware Proxy Hashing for Multi-Label Cross-Modal Retrieval” - QinLab-WFU/DSPH
Optimization of deep convolutional neural network for large scale image retrieval 2018, Neurocomputing Show abstract Content-based image retrieval: A review of recent trends 2021, Cogent Engineering Deep adversarial discrete hashing for cross-modal retrieval 2020, ICMR 2020 - Proceedings of the 2020 Int...