Linhua Ye

OW-DAR: Dual-Granularity Adaptive Reconstruction-Error Modeling for Open-World Object Detection

Open-world object detection (OWOD) aims to detect known and unknown objects in dynamic environments. However, only known classes are labeled during training, making it challenging for detectors to recognize unknown objects during inference. Existing methods typically rely on supervision from known c