Revisiting Human-in-the-Loop Object Retrieval with Pre-Trained Vision Transformers
Building on existing approaches, we revisit Human-in-the-Loop Object Retrieval, a task that consists of iteratively retrieving images containing objects of a class-of-interest, specified by a user-provided query. Starting from a large unlabeled image collection, the aim is to rapidly identify diverse instances of an object category relying solely on the initial query and the user's Relevance Feedback, with no prior labels. The retrieval process is formulated as a binary classification task, where the system continuously learns to distinguish between relevant and non-relevant images to the query, through iterative user interaction. This interaction is guided by an Active Learning loop: at ...
| Authors | Kawtar Zaher, Olivier Buisson, Alexis Joly |
|---|---|
| Primary category | cs.CV |
| Published | 1 April 2026 |
| Last updated at source | 29 April 2026 |
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