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 object-centered settings where global descriptors often suffice, multi-object images require more adapted, localized descriptors. In this work, we formulate and revisit the Human-in-the-Loop Object Retrieval task by leveraging pre-trained ViT representations, and addressing key design questions, including which object instances to consider in an image, what form the annotations should take, how Active Selection should be applied, and which representation strategies best capture the object's features. We compare several representation strategies across multi-object datasets highlighting trade-offs between capturing the global context and focusing on fine-grained local object details. Our results offer practical insights for the design of effective interactive retrieval pipelines based on Active Learning for object class retrieval.

翻译：基于现有方法，我们重新探讨了人机协同目标检索任务。该任务通过用户提供的查询迭代检索包含目标类别对象的图像。从大规模无标注图像集合出发，目标是在无先验标签的前提下仅依赖初始查询和用户的相关性反馈快速识别目标类别的多样化实例。我们将该检索过程形式化为二元分类任务：系统通过迭代的用户交互持续学习区分与查询相关的和非相关的图像。这一交互由主动学习循环引导：每次迭代中，系统选择信息量最大的样本用于用户标注，从而优化检索性能。该任务在多目标数据集中尤为具有挑战性——目标对象可能在复杂凌乱的场景中仅占据图像的小部分区域。与全局描述符即可满足需求的以对象为中心的场景不同，多目标图像需要更适配的局部化描述符。本研究通过利用预训练ViT表征重新审视人机协同目标检索任务，并解决关键设计问题：图像中应考虑哪些对象实例、标注应采取何种形式、如何应用主动选择策略、以及何种表征策略能最佳捕获对象特征。我们在多目标数据集上比较了多种表征策略，揭示了全局上下文捕获与细粒度局部目标细节聚焦之间的权衡。研究结果为基于主动学习构建面向目标类别检索的高效交互式检索管道提供了实践指导。