Cross-Modal Search for Social Networks via Adversarial Learning

Area of Science:

• Computer Science
• Artificial Intelligence
• Information Retrieval

Background:

• Cross-modal search is a growing research area, particularly for social networks.
• Social network data presents challenges like poor text quality, low-resolution images, and semantic sparseness, which can mislead traditional search methods.
• Existing cross-modal search techniques struggle with the unique characteristics of social media data.

Purpose of the Study:

• To propose a new cross-modal search method specifically designed for social network data.
• To address the limitations of data quality and semantic sparseness in social media information retrieval.
• To improve the accuracy and reliability of cross-modal search in social network environments.

Main Methods:

• The proposed method, cross-modal search with adversarial learning (CMSAL), utilizes self-attention-based neural networks.
• It generates modality-oriented representations to learn intermodal correlations.
• A search module employs adversarial learning, with a discriminator assessing feature distributions from both intramodal and intermodal perspectives.

Main Results:

• Experiments were conducted on real-world datasets from Sina Weibo and Wikipedia, which mimic social network properties.
• The CMSAL method demonstrated superior performance compared to existing state-of-the-art cross-modal search techniques.
• The results indicate the effectiveness of adversarial learning in improving cross-modal search for social data.

Conclusions:

• The proposed CMSAL method effectively handles the challenges of social network data for cross-modal search.
• Adversarial learning is a promising approach for enhancing intermodal correlation and search accuracy.
• The findings suggest a significant advancement in cross-modal information retrieval for social media platforms.