Open-Vocabulary Object Detection

Open-vocabulary object detection studies how to find and localize objects described by language, including categories that were not labeled during detector training. Common approaches align image regions with text using vision-language models such as CLIP, then improve localization and recognition through grounding, prompting, pseudo-labeling, or adaptation. Methods are commonly evaluated on benchmarks such as COCO and LVIS, with growing attention to fine-grained queries, domain shifts, and open-world settings where unknown objects may appear. A central challenge is making predictions reliable across wording and domains while calibrating confidence so that novel objects are not confused with background or familiar categories.