Open Vocabulary Object Detector

Open-vocabulary object detection aims to enable computer vision systems to identify objects not seen during training, using textual descriptions instead of predefined categories. Current research focuses on improving the robustness of these systems, particularly under challenging conditions like distribution shifts and fine-grained distinctions, often leveraging vision-language models (VLMs) like CLIP and architectures such as DINO and SAM, and exploring techniques like dynamic vocabulary construction and contrastive learning to enhance performance. This field is significant because it moves beyond the limitations of traditional object detection, paving the way for more adaptable and versatile computer vision applications in diverse domains, including remote sensing and robotics.