# Research on Cross-media Science and Technology Information Data Retrieval

arXiv: https://arxiv.org/abs/2204.04887 · HTML: https://mlsanity.com/paper/2204.04887/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 82 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [Recurrent Neural Network Regularization](https://mlsanity.com/paper/1409.2329/) (2014)
- [Self-Supervised Graph Co-Training for Session-based Recommendation](https://mlsanity.com/paper/2108.10560/) (2021)
- [Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition](https://mlsanity.com/paper/2103.16019/) (2021)
- [Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval](https://mlsanity.com/paper/2203.08615/) (2022)
- [CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning](https://mlsanity.com/paper/1710.05106/) (2017)

## Abstract (arXiv)

Since the era of big data, the Internet has been flooded with all kinds of information. Browsing information through the Internet has become an integral part of people's daily life. Unlike news data and social data on the Internet, cross-media science and technology information data has different characteristics. This data has become an important basis for researchers and scholars to track current hot spots and explore future directions of technology development. As the volume of science and technology information data becomes richer, traditional science and technology information retrieval systems, which support only unimodal data retrieval and use outdated keyword-matching models, can no longer meet the daily retrieval needs of science and technology scholars. Therefore, in view of this research background, it is of profound practical significance to study cross-media science and technology information data retrieval systems based on deep semantic features, in line with domestic and international technology-development trends.