From the perspective of class space on base set, existing methods either focus on utilizing all classes under a global view by normal pretraining, or pay more attention to adopt an episodic manner to train meta-tasks within few classes in a local view.
Few-shot object detection, which aims at detecting novel objects rapidly from extremely few annotated examples of previously unseen classes, has attracted significant research interest in the community.
Ranked #2 on Few-Shot Object Detection on MS-COCO (1-shot)
%We argue that such flexibility is also important for deep metric learning, because different visual concepts indeed correspond to different semantic scales.
We present a novel self quality evaluation metric SQE for parameters optimization in the challenging yet critical multi-object tracking task.