Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Text-image Retrieval

Concepts de base

Creating a new dataset, Flickr30K-CFQ, addresses the limitations of existing text-image retrieval datasets by introducing compact and fragmented queries for more natural and diverse retrieval tasks.

Résumé

The content introduces the Flickr30K-CFQ dataset for text-image retrieval, highlighting the need for more natural query expressions. It discusses the challenges in existing datasets, proposes a novel LLM-based Query-enhanced method, and presents experimental results showcasing the effectiveness of the new dataset and method.
Directory:

Introduction

Text-image retrieval process across different modalities.
Traditional methods vs. pre-trained models with large language models (LLMs).

Related Work

Overview of datasets for text-image retrieval.

Dataset Construction

Introduction of Flickr30K-CFQ with four-level granularities query corpus.

LLM-based Query-enhanced Method

Proposal of a method using LLMs to enhance query understanding.

Experiment

Evaluation of proposed method on Flickr30K-CFQ and public benchmarks.

Conclusion

Summary of contributions and implications.

Stats

Experiments show improvements on public dataset and challenge set with over 0.9% and 2.4% respectively.

Citations

"Existing text-image retrieval research is mostly based on general vision-language datasets."
"Our project reveals the insufficiency of existing vision-language datasets in realistic text-image tasks."

Idées clés tirées de

Flickr30K-CFQ

by Haoyu Liu,Ya... à arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13317.pdf

Questions plus approfondies

How can the concept of compact and fragmented queries be applied to other areas beyond text-image retrieval

The concept of compact and fragmented queries can be applied to various other areas beyond text-image retrieval, especially in the realm of information retrieval systems. For instance:

Web Search: Users often input short, concise queries into search engines. By understanding and processing these compact queries effectively, search engines can provide more relevant and accurate results.
E-commerce: In online shopping platforms, users may use fragmented descriptions or specific keywords to find products. Enhancing query understanding for such queries can improve product recommendations and user experience.
Healthcare: Medical professionals might need to search for specific medical terms or symptoms quickly. Utilizing compact and fragmented queries can assist in retrieving relevant research articles or patient records efficiently.
By incorporating the principles of compactness and fragmentation in query design across different domains, information retrieval systems can better cater to users' needs by providing precise and targeted results.

What are potential drawbacks or criticisms of using large language models (LLMs) in enhancing query understanding

While large language models (LLMs) offer significant benefits in enhancing query understanding, there are potential drawbacks and criticisms associated with their usage:

Computational Resources: Training LLMs requires substantial computational resources which may not be accessible to all researchers or organizations.
Bias Amplification: LLMs have been known to amplify biases present in the training data, leading to biased outputs that could perpetuate societal inequalities.
Lack of Interpretability: The inner workings of LLMs are complex, making it challenging to interpret how they arrive at certain conclusions or decisions.
Ethical Concerns: There are ethical considerations surrounding the use of LLMs, particularly regarding privacy violations through data collection and potential misuse for malicious purposes.
Addressing these criticisms is crucial for ensuring responsible deployment of LLM-based technologies while maximizing their benefits in enhancing query understanding.

How might advancements in natural language processing impact future developments in text-image retrieval

Advancements in natural language processing (NLP) are poised to revolutionize future developments in text-image retrieval by introducing innovative techniques and capabilities:

Improved Semantic Understanding: Enhanced NLP models will enable deeper semantic analysis of textual content related to images, leading to more accurate cross-modal matching between text descriptions and visual elements.
Efficient Query Expansion Techniques: Advanced NLP algorithms can facilitate dynamic expansion of queries based on contextually relevant information extracted from both textual descriptions and image features.
Enhanced Multimodal Fusion Models: Future advancements may focus on developing sophisticated multimodal fusion models that leverage state-of-the-art NLP techniques for seamless integration of text-based cues with visual representations during retrieval tasks.
Overall, progress in NLP is expected to drive significant enhancements in text-image retrieval systems by enabling more nuanced query understanding mechanisms that bridge the gap between textual inputs and visual content effectively.