Personalized Object Identification and Localization via In-Context Inference with Vision-Language Models

arXiv cs.CV1mo4 min read

arXiv:2607.00357v1 Announce Type: new Abstract: Personalized object localization (POL) localizes an object instance in a query image based on a few reference images with bounding-box annotations and a target object label. The pioneering method, IPLoc, solves this task through in-context inference with vision-language models (VLMs). However, it assumes that the query image always contains the target object. This assumption severely limits its applicability to real-world scenarios with many irrelevant images. To address this issue, we formulate a new task, personalized object identification and

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Personalized Object Identification and Localization via In-Context Inference with Vision-Language Models

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