Yasiru Ranasinghe

Human annotation on the target dataset is replaced by an automatic pipeline: SAM produces segmentation proposals, CLIP pre-filters and matches labels, and a large vision-language model labels only cluster medoid images, after which an open-set detector is trained on those pseudo-labels and refined iteratively. Under zero-shot settings on COCO val2017 and LVIS minival, the YOLO-backbone variant reaches 60.8 AP and 47.4 AP, while the Swin-T variant reaches 57.2 AP and 46.2 AP, indicating the gain is not only from the detector backbone.

Human annotation on the target dataset is replaced by an automatic pipeline: SAM produces segmentation proposals, CLIP pre-filters and matches labels, and a large vision-language model labels only cluster medoid images, after which an open-set detector is trained on those pseudo-labels and refined iteratively. Under zero-shot settings on COCO val2017 and LVIS minival, the YOLO-backbone variant reaches 60.8 AP and 47.4 AP, while the Swin-T variant reaches 57.2 AP and 46.2 AP, indicating the gain is not only from the detector backbone.