Open World Object Localization Based on Few Shot Learning of Unknown Categories
Unknown category samples are difficult to obtain in open world object recognition,and these samples cannot effectively participate in training.To address this issue,this study proposes an open-world object localization method based on few-shot learning for unknown categories.Initially,a pseudo-label generation model is employed to label regions with high objectness scores within background areas as pseudo-labels,providing a small number of samples for the model to learn unknown classes and enhancing its generalization ability towards unseen categories.Subsequently,two branches are designed to jointly learn object features:one focuses on determining the presence of an object within the bounding box(objectness branch),while the other concentrates on assessing the quality of the bounding box(localization quality branch).These branches extract and comprehend object features from two dimensions,i.e.object existence and localization quality.The objectness branch is trained jointly with both real and pseudo-label samples to enhance recognition of unknown categories,whereas the localization quality branch learns from high-quality real labels and discards pseudo-labels to minimize noise interference.An effective evaluation of this method on the COCO dataset demonstrates that it has excellent detection performance compared to other methods.
open worldobject detectionobject localizationpseudo-labellocalization quality
万方数据
维普
