Core Concepts
Integrating CLIP and relevance feedback techniques can enhance the accuracy and adaptability of interactive image retrieval systems, overcoming the limitations of metric learning-based approaches.
Abstract
The paper proposes an interactive image retrieval system that combines the Contrastive Language-Image Pre-training (CLIP) model and relevance feedback techniques. The key aspects are:
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Retrieval Pipeline:
- The system first retrieves similar images using a CLIP image encoder and collects user feedback on the returned samples.
- It then updates the retrieval algorithm based on the user feedback and returns more relevant images.
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Proposed Method:
- The system uses the CLIP image encoder as the visual encoder and updates the retrieval algorithm by predicting user preferences based on the feedback.
- This allows the system to adapt to each user's unique preferences without requiring additional training.
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Evaluation:
- The authors evaluate the system on category-based image retrieval, one-label-based image retrieval, and conditioned image retrieval tasks.
- They show that the proposed system achieves competitive or better performance compared to state-of-the-art metric learning and multimodal retrieval methods, despite not training the image encoder specifically for each dataset.
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Additional Analysis:
- The authors investigate the impact of CLIP encoder architecture and feedback size on the retrieval accuracy.
- They also analyze the relationship between the number of positive feedback and the retrieval performance, as well as the runtime of the proposed system.
Overall, the paper demonstrates the potential benefits of integrating CLIP with classic relevance feedback techniques to enhance the accuracy and adaptability of interactive image retrieval systems.
Stats
The paper does not provide any specific numerical data or statistics. The results are presented in the form of Recall@K metrics for various experimental settings.
Quotes
"Our retrieval system successfully adapts to each user's preference through the feedback and achieves high accuracy without training."
"With a realistic feedback size, our retrieval system achieves competitive results with state-of-the-art multimodal retrieval in conditioned image retrieval settings, despite not exploiting textual information."