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Deep Learning for Instance Retrieval: A Survey.

Wei Chen, Yu Liu, Weiping Wang

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    Summary
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    This survey reviews deep learning techniques for Content Based Image Retrieval (CBIR), focusing on feature extraction, embedding, and network tuning for more efficient and accurate instance searches in large visual datasets.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Information Retrieval

    Background:

    • The exponential growth of visual content across diverse fields (social media, medical imaging, robotics) necessitates advanced methods for searching similar images.
    • Content Based Image Retrieval (CBIR) is crucial for efficiently searching large visual databases, but current methods require improvements in speed and accuracy for real-time applications.

    Purpose of the Study:

    • To provide a comprehensive survey of recent advancements in instance retrieval within CBIR, specifically those leveraging deep learning algorithms.
    • To organize and analyze deep learning-based CBIR methods by their core components: feature extraction, embedding/aggregation, and network fine-tuning.

    Main Methods:

    • Review and categorization of recent deep learning techniques applied to instance retrieval in CBIR.
    • Analysis organized around deep feature extraction, feature embedding and aggregation, and network fine-tuning strategies.
    • Identification of milestone works, interconnections between methods, and common benchmarks.

    Main Results:

    • Highlights progress in AI-driven CBIR, particularly in instance search capabilities.
    • Identifies key deep learning approaches and their impact on retrieval efficiency and accuracy.
    • Presents common benchmarks and evaluation results for a wide range of recent methods.

    Conclusions:

    • Deep learning has significantly advanced the field of Content Based Image Retrieval, enabling more effective instance searches.
    • The survey provides a structured overview of current deep learning strategies, common challenges, and identifies promising avenues for future research in CBIR.