Detection Transformer

Detection Transformer studies object detectors that use transformer encoders and decoders to turn image features into a set of object classes and locations. Object queries represent candidate objects, while one-to-one assignment—often Hungarian matching—trains predictions without conventional anchors or non-maximum suppression. Researchers improve these models with deformable attention, stronger multi-scale features, better query selection and matching, and adaptations for tasks such as small-object, oriented, 3D, and open-vocabulary detection. A central challenge is balancing accuracy and generalization against training and inference cost, especially in real-time or high-resolution settings.