SBV improves open-vocabulary detection by adaptively sampling semantic concept windows, switching between global, middle, and local views, and masking CLIP attention, gaining 2.6 AP50 on COCO and 0.5 mAP on LVIS over BARON.
Learning to prompt for open-vocabulary ob- ject detection with vision-language model
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Sampling Bag of Views for Open-Vocabulary Object Detection
SBV improves open-vocabulary detection by adaptively sampling semantic concept windows, switching between global, middle, and local views, and masking CLIP attention, gaining 2.6 AP50 on COCO and 0.5 mAP on LVIS over BARON.