Summary of Context-based Semantic-aware Alignment For Semi-supervised Multi-label Learning, by Heng-bo Fan et al.
Context-Based Semantic-Aware Alignment for Semi-Supervised Multi-Label Learning
by Heng-Bo Fan, Ming-Kun Xie, Jia-Hao Xiao, Sheng-Jun Huang
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract proposes a novel approach to semi-supervised multi-label learning (SSMLL) that leverages pre-trained vision-language models (VLMs). Existing methods have achieved advances in weakly-supervised multi-label learning, but they failed to fully utilize the information from labeled data to enhance the learning of unlabeled data. The proposed method, context-based semantic-aware alignment, aims to address this challenge by introducing a novel framework design that extracts label-specific image features and achieves a more compact alignment between text features and label-specific image features, generating high-quality pseudo-labels. To incorporate comprehensive understanding of images, the model is designed with a semi-supervised context identification auxiliary task that captures co-occurrence information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed method uses pre-trained VLMs to alleviate the challenge of limited labeled data in SSMLL. The approach involves extracting label-specific image features and achieving a more compact alignment between text features and label-specific image features, generating high-quality pseudo-labels. This method is tested on multiple benchmark datasets and shows promising results. |
Keywords
» Artificial intelligence » Alignment » Semi supervised » Supervised
