Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism

Publication Type:
Conference Proceeding
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, 2022-June, pp. 9245-9254
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Continual learning is a challenging real world problem for constructing a mature AI system when data are provided in a streaming fashion Despite recent progress in continual classification the researches of continual object detection are impeded by the diverse sizes and numbers of objects in each image Different from previous works that tune the whole network for all tasks in this work we present a simple and flexible framework for continual object detection via pRotOtypical taSk corrElaTion guided gaTing mechAnism ROSETTA Concretely a unified framework is shared by all tasks while task aware gates are introduced to automatically select sub models for specific tasks In this way various knowledge can be successively memorized by storing their corresponding sub model weights in this system To make ROSETTA automatically determine which experience is available and useful a prototypical task correlation guided Gating Diversity Controller GDC is introduced to adaptively adjust the diversity of gates for the new task based on class specific prototypes GDC module computes class to class correlation matrix to depict the cross task correlation and hereby activates more exclusive gates for the new task if a significant domain gap is observed Comprehensive experiments on COCO VOC KITTI Kitchen class incremental detection on VOC and sequential learning of four tasks show that ROSETTA yields state of the art performance on both task based and class based continual object detection 11Codes are available at https github com dkxocl ROSSETA
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