Despite the recent advances in the field of object detection, common architectures are still ill-suited to incrementally detect new categories over time. They are vulnerable to catastrophic forgetting: they forget what has been already learned while updating their parameters in absence of the original training data. Previous works extended standard classification methods in the object detection task, mainly adopting the knowledge distillation framework. However, we argue that object detection introduces an additional problem, which has been overlooked. While objects belonging to new classes are learned thanks to their annotations, if no supervision is provided for other objects that may still be present in the input, the model learns to associate them to background regions. We propose to handle these missing annotations by revisiting the standard knowledge distillation framework. Our approach outperforms current state-of-the-art methods in every setting of the Pascal-VOC dataset. We further propose an extension to instance segmentation, outperforming the other baselines. Code can be found here: https://github.com/fcdl94/MMA
Modeling Missing Annotations for Incremental Learning in Object Detection / Cermelli, Fabio; Geraci, Antonino; Fontanel, Dario; Caputo, Barbara. - ELETTRONICO. - IEEE/CVF Computer Vision and Pattern Recognition (Workshop CLVISION):(2022), pp. 3699-3709. (Intervento presentato al convegno Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition tenutosi a New Orleans (USA) nel 19-20 June 2022) [10.1109/CVPRW56347.2022.00414].
Modeling Missing Annotations for Incremental Learning in Object Detection
Fabio Cermelli;Antonino Geraci;Dario Fontanel;Barbara Caputo
2022
Abstract
Despite the recent advances in the field of object detection, common architectures are still ill-suited to incrementally detect new categories over time. They are vulnerable to catastrophic forgetting: they forget what has been already learned while updating their parameters in absence of the original training data. Previous works extended standard classification methods in the object detection task, mainly adopting the knowledge distillation framework. However, we argue that object detection introduces an additional problem, which has been overlooked. While objects belonging to new classes are learned thanks to their annotations, if no supervision is provided for other objects that may still be present in the input, the model learns to associate them to background regions. We propose to handle these missing annotations by revisiting the standard knowledge distillation framework. Our approach outperforms current state-of-the-art methods in every setting of the Pascal-VOC dataset. We further propose an extension to instance segmentation, outperforming the other baselines. Code can be found here: https://github.com/fcdl94/MMAFile | Dimensione | Formato | |
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https://hdl.handle.net/11583/2970193