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Reading Papers: CIReVL (ICLR 2024)

·370 words·2 mins·
Table of Contents

Vision-by-Language for Training-Free Compositional Image Retrieval
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TL;DR: A training-free, modular, and interpretable approach that leverages LLMs to enhance text-based queries for compositional image retrieval.

Note: This is my personal learning note, so some points may not be entirely accurate. I strive to improve my understanding and will correct any errors I find. If you spot any inaccuracies, please feel free to share your insights to help enhance the content 😊.

Summary
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The paper introduces CIReVL (Compositional Image Retrieval through Vision-by-Language), a novel zero-shot compositional image retrieval (ZS-CIR) method. CIReVL leverages existing pre-trained vision-language models (VLMs) and large language models (LLMs) to perform CIR without any additional training.

Motivation
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Traditional CIR approaches rely on supervised training with annotated triplets (query image, modifying text, target image), which are expensive and time-consuming to obtain. Existing ZS-CIR methods often still require training task-specific modules on large image-caption datasets. CIReVL addresses these limitations by using only pre-trained models in a modular, interpretable fashion, offering a more accessible alternative.

Methodology
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CIReVL Workflow

CIReVL operates through three main steps:

  1. Captioning: A pre-trained VLM (e.g., BLIP-2) generates a detailed caption for the query image.
  2. Reasoning: An LLM (e.g., GPT-3.5-turbo) processes the generated caption and modification instruction to produce a target caption that reflects the intended changes.
  3. Retrieval: A second VLM (e.g., CLIP) encodes the target caption and the database images to retrieve the image most similar to the target description.

Why CIReVL Stands Out
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  • Training-Free: CIReVL doesn’t require any additional training, relying entirely on off-the-shelf pre-trained models, which enhances efficiency and scalability.
  • Modular Design: This modular approach allows easy replacement or scaling of individual components, enabling exploration of different VLMs and LLMs.
  • Human-Interpretable: Most of the compositional processing occurs in the language domain, making it interpretable and allowing for human intervention in case of potential errors.
  • State-of-the-Art Performance: CIReVL matches or surpasses existing training-based and zero-shot methods on several CIR benchmarks, including CIRCO, CIRR, FashionIQ, and GeneCIS.

Conclusion
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CIReVL presents a promising new direction for zero-shot CIR research. Its training-free, modular, and interpretable design, along with its high performance, makes it a compelling approach for future work in ZS-CIR.

Reference
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Bach-Khoi Vo
Author
Bach-Khoi Vo
Hi 👋 I’m an AI engineer with a love for building practical, production-ready AI systems, especially around Large Language Models (LLMs) and MLOps. I enjoy exploring the latest in Retrieval-Augmented Generation, NLP, and multi-modal AI. My journey has taken me from research and hands-on projects to sharing insights through open-source contributions and technical writing. Whether it’s developing new models, designing pipelines, or diving into cloud tech, I’m always eager to learn and connect with others in the AI space!