Zero-shot Composed Text-Image Retrieval Study

Core Concepts

The authors propose a retrieval-based pipeline for automatic dataset construction and introduce TransAgg, a transformer-based adaptive aggregation model, showing competitive performance in zero-shot composed image retrieval.

Abstract

This study focuses on composed image retrieval (CIR) using text and images, proposing a scalable dataset construction method and an adaptive aggregation model. Extensive experiments demonstrate the effectiveness of the proposed approach in achieving state-of-the-art results in zero-shot scenarios.

In recent literature, vision-language models have shown progress in joint training of image and text representations. The study aims to improve image retrieval by leveraging reference images and relative captions. Existing approaches require manually constructed datasets for CIR training, which can be costly and time-consuming.

The authors introduce a scalable pipeline to automatically construct datasets for CIR training using large-scale image-caption data available online. They propose TransAgg, a transformer-based model that dynamically fuses information from different modalities. Results show that the proposed approach outperforms existing state-of-the-art models in zero-shot composed image retrieval benchmarks.

Key metrics or figures used to support the argument:

• Recall@K values for different backbones and fine-tuning types.
• Performance comparison with other methods on CIRR and FashionIQ datasets.

Stats

Zero-shot evaluation is conducted on two public benchmarks: CIRR and FashionIQ.
Proposed method achieves competitive results compared to existing approaches.
Different backbones and fine-tuning types are evaluated for performance metrics.

Quotes

"The practical datasets for training CIR models tend to be limited by scale."
"Our proposed approach either performs on par with or significantly outperforms the existing state-of-the-art (SOTA) models."

Key Insights Distilled From

Zero-shot Composed Text-Image Retrieval

by Yikun Liu,Ji... at arxiv.org 03-07-2024

https://arxiv.org/pdf/2306.07272.pdf

Deeper Inquiries

How can the proposed dataset construction method be further improved

The proposed dataset construction method can be further improved by incorporating more sophisticated natural language processing techniques. For instance, leveraging advanced semantic analysis algorithms to ensure that the edited captions accurately reflect the desired modifications in the reference images. Additionally, integrating human feedback or validation mechanisms into the dataset construction pipeline could enhance the quality and relevance of the generated triplets. Furthermore, exploring diverse templates and rules for caption editing across a wider range of semantic operations could enrich the dataset with more varied and contextually relevant samples.

What implications does this study have for real-world applications of image-text retrieval systems

This study has significant implications for real-world applications of image-text retrieval systems, particularly in e-commerce, visual search engines, and content recommendation platforms. By enabling accurate composed image retrieval based on both visual features and textual descriptions, this approach enhances user experience by providing more precise search results tailored to individual preferences. In e-commerce settings, it can facilitate better product recommendations based on specific attributes described in text or depicted visually. Moreover, in content creation tools or social media platforms, it can assist users in finding relevant images based on detailed textual cues.

How might advancements in language models impact the future of zero-shot composed image retrieval

Advancements in language models are poised to revolutionize zero-shot composed image retrieval by enhancing the understanding of complex textual queries and improving cross-modal interactions between text and images. With more powerful language models capable of generating highly contextualized descriptions and instructions for modifying images accurately, zero-shot retrieval systems can achieve higher levels of performance without extensive training data requirements. The future integration of state-of-the-art language models like GPT-4 or advanced transformer architectures could lead to even greater accuracy and efficiency in retrieving target images based on nuanced user inputs across various domains such as fashion, product design, art curation etc.