Transformers Take Over Object Detection

Transformers, initially introduced in 2017 for natural language processing, have made significant strides in artificial intelligence, particularly in computer vision, by enhancing object detection capabilities. Microsoft's DyHead has achieved state-of-the-art performance using a Transformer backbone, outperforming previous methods on the COCO benchmark. The evolution of Transformers began with their application in NLP, where models like BERT and GPT demonstrated their ability to predict sequences and mask words, which led to their adaptation in vision tasks. Vision Transformers (ViT) and models like CLIP have further advanced the field by integrating text and image processing, resulting in a web-scale semantic understanding. DyHead's research focuses on directing attention to image features for object detection, marking a notable improvement by incorporating Transformer backbones over traditional CNNs. As Transformers continue to transform AI, their application in tasks such as instance segmentation is anticipated to evolve further.