Detector Training

Detector training focuses on improving the accuracy and efficiency of object detection models, aiming to minimize the need for extensive, manually labeled datasets. Current research emphasizes developing methods for handling noisy annotations, adapting pre-trained models to new classes (open-vocabulary detection), and enhancing existing architectures like DETR through refinement techniques or integrating pre-trained transformer encoder-decoders. These advancements are crucial for expanding the applicability of object detection to diverse real-world scenarios, particularly in robotics and situations with limited labeled data, while also addressing privacy concerns related to training data.