Replay Consolidation with Label Propagation for Continual Object Detection

• Main Authors: De Monte, Riccardo, Pezze, Davide Dalle, Ceccon, Marina, Pasti, Francesco, Paissan, Francesco, Farella, Elisabetta, Susto, Gian Antonio, Bellotto, Nicola
• Format: Journal Article
• Language: English
• Published: 09-09-2024
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2409.05650

Object Detection is a highly relevant computer vision problem with many
applications such as robotics and autonomous driving. Continual Learning~(CL)
considers a setting where a model incrementally learns new information while
retaining previously acquired knowledge. This is particularly challenging since
Deep Learning models tend to catastrophically forget old knowledge while
training on new data. In particular, Continual Learning for Object
Detection~(CLOD) poses additional difficulties compared to CL for
Classification. In CLOD, images from previous tasks may contain unknown classes
that could reappear labeled in future tasks. These missing annotations cause
task interference issues for replay-based approaches. As a result, most works
in the literature have focused on distillation-based approaches. However, these
approaches are effective only when there is a strong overlap of classes across
tasks. To address the issues of current methodologies, we propose a novel
technique to solve CLOD called Replay Consolidation with Label Propagation for
Object Detection (RCLPOD). Based on the replay method, our solution avoids task
interference issues by enhancing the buffer memory samples. Our method is
evaluated against existing techniques in CLOD literature, demonstrating its
superior performance on established benchmarks like VOC and COCO.