Revisiting Human-in-the-loop Object Retrieval With Pre-trained Vision Transformers

Abstract

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 each iteration, the system selects informative samples for user annotation, thereby refining the retrieval performance. This task is particularly challenging in multi-object datasets, where the object of interest may occupy only a small region of the image within a complex, cluttered scene. Unlike obje

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyzaher2026revisiting

Related papers

• Training Vision Transformers For Image Retrieval (2021)0.00
• Boosting Vision Transformers For Image Retrieval (2022)15.28
• Object-aware Video-language Pre-training For Retrieval (2021)16.14
• FOR: Finetuning For Object Level Open Vocabulary Image Retrieval (2024)0.00
• Object-centric Open-vocabulary Image-retrieval With Aggregated Features (2023)0.00
• Why-so-deep: Towards Boosting Previously Trained Models For Visual Place Recognition (2022)7.81
• Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models And Vision Language Models (2024)8.82
• Instance-level Image Retrieval Using Reranking Transformers (2021)19.00