Replay Consolidation with Label Propagation for Continual Object Detection

• URL: http://arxiv.org/abs/2409.05650v3
• Date: Tue, 04 Mar 2025 10:22:01 GMT
• Title: Replay Consolidation with Label Propagation for Continual Object Detection
• Authors: Riccardo De Monte, Davide Dalle Pezze, Marina Ceccon, Francesco Pasti, Francesco Paissan, Elisabetta Farella, Gian Antonio Susto, Nicola Bellotto,
• Abstract summary: Continual Learning aims to learn new data while remembering previously acquired knowledge.<n>In this scenario, images from previous tasks may contain instances of unknown classes that could reappear as labeled in future tasks.<n>We propose an alternative to distillation-based approaches with a novel approach called Replay Consolidation with Label propagation for Object Detection.
• Score: 7.454468349023651
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continual Learning (CL) aims to learn new data while remembering previously acquired knowledge. In contrast to CL for image classification, CL for Object Detection faces additional challenges such as the missing annotations problem. In this scenario, images from previous tasks may contain instances of unknown classes that could reappear as labeled in future tasks, leading to task interference in replay-based approaches. Consequently, most approaches in the literature have focused on distillation-based techniques, which are effective when there is a significant class overlap between tasks. In our work, we propose an alternative to distillation-based approaches with a novel approach called Replay Consolidation with Label Propagation for Object Detection (RCLPOD). RCLPOD enhances the replay memory by improving the quality of the stored samples through a technique that promotes class balance while also improving the quality of the ground truth associated with these samples through a technique called label propagation. RCLPOD outperforms existing techniques on well-established benchmarks such as VOC and COC. Moreover, our approach is developed to work with modern architectures like YOLOv8, making it suitable for dynamic, real-world applications such as autonomous driving and robotics, where continuous learning and resource efficiency are essential.

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