Data Augmentation for Text-based Person Retrieval Using Large Language Models
Authors: Zheng Li, Lijia Si, Caili Guo, Yang Yang, Cao Qiushi


Reviewer paper summaries: This paper introduces a novel method for improving Text-based Person Retrieval (TPR) models by using Large Language Models (LLMs) for data augmentation. The proposed LLM-based Data Augmentation (LLM-DA) method rewrites text in existing TPR datasets to increase text diversity while maintaining key semantic concepts. To address LLM hallucinations, a Text Faithfulness Filter is included to ensure the accuracy of the augmented text. Additionally, a Balanced Sampling Strategy is proposed to optimize the use of original and augmented texts during training. LLM-DA is designed to be easily integrated into various TPR models, and comprehensive experiments demonstrate its effectiveness in enhancing retrieval performance. This approach offers a cost-effective and efficient solution to the challenge of limited high-quality data in TPR tasks.
Summary of strengths: - LLM-Based Data Augmentation (LLM-DA): The paper introduces a pioneering method for text-based person retrieval (TPR) using large language models (LLMs) for data augmentation. This method leverages the ability of LLMs to rewrite text, significantly enhancing the quality and diversity of TPR datasets.
- To address the common issue of hallucinations in LLM outputs, the paper proposes a Text Faithfulness Filter. This ensures that the augmented text remains faithful to the original semantics, which is crucial for maintaining the integrity of the dataset.
- To optimize the training process, the paper introduces a Balanced Sampling Strategy. This strategy carefully manages the proportion of original and augmented texts, ensuring a balanced contribution to the model's training.
Summary of weaknesses: 1. The author solely uses CLIP as the baseline model, without employing newer models or multiple models for comparison, which makes it difficult to comprehensively demonstrate the generalization ability of the proposed method.
2. In terms of data augmentation methods, the comparison is limited to three outdated techniques, which may not be sufficient to prove the superiority of the proposed method.
3. The author describes this method as having a plug-and-play characteristic, but it seems to require extensive parameter tuning.
Comments, suggestions and typos: - The authors should use updated encoders to demonstrate the effectiveness of their work.
- The authors should compare their method with more data augmentation techniques to prove its effectiveness.
- The authors could further demonstrate the effectiveness of their proposed method by visualizing the distribution after data augmentation.
Overall assessment scores: 2.5