Researchers revisit human-in-the-loop object retrieval using Vision Transformers

Researchers have revisited the task of Human-in-the-Loop Object Retrieval, a method for iteratively finding images with specific objects using user feedback. The process involves a system learning to distinguish relevant images through user annotations, guided by an Active Learning loop. This approach is particularly useful for complex, cluttered images where the target object is small, and the paper explores different representation strategies using pre-trained Vision Transformers to balance global context with local object details. AI

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IMPACT Explores new methods for interactive image retrieval, potentially improving how users find specific objects in large, complex datasets.

RANK_REASON This is a research paper published on arXiv detailing a new approach to object retrieval.

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• arXiv
• Kawtar Zaher
• Vision Transformers

• paper
• other