Grounding DINO: Achieving SOTA Zero-Shot Learning Object Detection

Enabling Robust and Flexible Detection of Novel Objects without Extensive Retraining or Data Collection

The lack of flexibility is a major challenge with most object detection models as they are specifically trained to recognize only a limited set of predetermined classes. Expanding or modifying the list of identifiable objects requires collecting and labeling additional data, followed by retraining the model from scratch, which is inevitably a time-consuming and costly process.

The aim of zero-shot detectors is to disrupt the current …

computer vision deeplearing detection generative-ai segmentation