# Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval

arXiv: https://arxiv.org/abs/2203.08615 · HTML: https://mlsanity.com/paper/2203.08615/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 44 references that are in the map.
- First posted on arXiv 16 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks](https://mlsanity.com/paper/1709.07592/) (2017)
- [Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition](https://mlsanity.com/paper/2103.16019/) (2021)
- [Transfer Learning with Dynamic Adversarial Adaptation Network](https://mlsanity.com/paper/1909.08184/) (2019)
- [Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval](https://mlsanity.com/paper/1804.01223/) (2018)
- [Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks](https://mlsanity.com/paper/1909.06627/) (2019)
- [An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges](https://mlsanity.com/paper/1704.02223/) (2017)
- [Modality-dependent Cross-media Retrieval](https://mlsanity.com/paper/1506.06628/) (2015)

## Built on by (most cited first, 30 of 33)

- [Chinese Word Sense Embedding with SememeWSD and Synonym Set](https://mlsanity.com/paper/2206.14388/) (2022)
- [Semantic Similarity Computing for Scientific Academic Conferences fused with domain features](https://mlsanity.com/paper/2203.12593/) (2022)
- [Dynamic Fair Federated Learning Based on Reinforcement Learning](https://mlsanity.com/paper/2311.00959/) (2023)
- [An Intellectual Property Entity Recognition Method Based on Transformer and Technological Word Information](https://mlsanity.com/paper/2203.10717/) (2022)
- [Prediction Algorithm for Heat Demand of Science and Technology Topics Based on Time Convolution Network](https://mlsanity.com/paper/2203.10718/) (2022)
- [Research Scholar Interest Mining Method based on Load Centrality](https://mlsanity.com/paper/2203.10731/) (2022)
- [Academic Resource Text Level Multi-label Classification based on Attention](https://mlsanity.com/paper/2203.10743/) (2022)
- [Web Page Content Extraction Based on Multi-feature Fusion](https://mlsanity.com/paper/2203.12591/) (2022)
- [Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model](https://mlsanity.com/paper/2203.15595/) (2022)
- [Research topic trend prediction of scientific papers based on spatial enhancement and dynamic graph convolution network](https://mlsanity.com/paper/2203.16256/) (2022)
- [An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers](https://mlsanity.com/paper/2203.16751/) (2022)
- [Scientific and Technological Text Knowledge Extraction Method of based on Word Mixing and GRU](https://mlsanity.com/paper/2203.17079/) (2022)
- [Accurate Portraits of Scientific Resources and Knowledge Service Components](https://mlsanity.com/paper/2204.04883/) (2022)
- [Research on Cross-media Science and Technology Information Data Retrieval](https://mlsanity.com/paper/2204.04887/) (2022)
- [Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences](https://mlsanity.com/paper/2204.04888/) (2022)
- [Retrieval of Scientific and Technological Resources for Experts and Scholars](https://mlsanity.com/paper/2204.06142/) (2022)
- [Research on Intellectual Property Resource Profile and Evolution Law](https://mlsanity.com/paper/2204.06221/) (2022)
- [Research on Domain Information Mining and Theme Evolution of Scientific Papers](https://mlsanity.com/paper/2204.08476/) (2022)
- [GRAPHYP: A Scientific Knowledge Graph with Manifold Subnetworks of Communities. Detection of Scholarly Disputes in Adversarial Information Routes](https://mlsanity.com/paper/2205.01331/) (2022)
- [Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon](https://mlsanity.com/paper/2206.02160/) (2022)
- [A sentiment analysis model for car review texts based on adversarial training and whole word mask BERT](https://mlsanity.com/paper/2206.02389/) (2022)
- [Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting](https://mlsanity.com/paper/2206.03128/) (2022)
- [A Rare Topic Discovery Model for Short Texts Based on Co-occurrence word Network](https://mlsanity.com/paper/2207.00432/) (2022)
- [Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa](https://mlsanity.com/paper/2207.02424/) (2022)
- [Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property](https://mlsanity.com/paper/2210.05947/) (2022)
- [Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints](https://mlsanity.com/paper/2306.12857/) (2023)
- [Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering](https://mlsanity.com/paper/2306.12859/) (2023)
- [Semantic Representation Learning of Scientific Literature based on Adaptive Feature and Graph Neural Network](https://mlsanity.com/paper/2311.00296/) (2023)
- [Entity Alignment Method of Science and Technology Patent based on Graph Convolution Network and Information Fusion](https://mlsanity.com/paper/2311.00300/) (2023)
- [Federated Topic Model and Model Pruning Based on Variational Autoencoder](https://mlsanity.com/paper/2311.00314/) (2023)

## Abstract (arXiv)

Cross-media retrieval of scientific and technological information is one of the important tasks in the cross-media study. Cross-media scientific and technological information retrieval obtain target information from massive multi-source and heterogeneous scientific and technological resources, which helps to design applications that meet users' needs, including scientific and technological information recommendation, personalized scientific and technological information retrieval, etc. The core of cross-media retrieval is to learn a common subspace, so that data from different media can be directly compared with each other after being mapped into this subspace. In subspace learning, existing methods often focus on modeling the discrimination of intra-media data and the invariance of inter-media data after mapping; however, they ignore the semantic consistency of inter-media data before and after mapping and media discrimination of intra-semantics data, which limit the result of cross-media retrieval. In light of this, we propose a scientific and technological information oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval method (SMCR) to find an effective common subspace. Specifically, SMCR minimizes the loss of inter-media semantic consistency in addition to modeling intra-media semantic discrimination, to preserve semantic similarity before and after mapping. Furthermore, SMCR constructs a basic feature mapping network and a refined feature mapping network to jointly minimize the media discriminative loss within semantics, so as to enhance the feature mapping network's ability to confuse the media discriminant network. Experimental results on two datasets demonstrate that the proposed SMCR outperforms state-of-the-art methods in cross-media retrieval.