Better Matching, Less Forgetting: A Quality-Guided Matcher for Transformer-based Incremental Object Detection
Incremental Object Detection (IOD) aims to continuously learn new object classes without forgetting previously learned ones. A persistent challenge is catastrophic forgetting, primarily attributed to background shift in conventional detectors. While pseudo-labeling mitigates this in dense detectors, we identify a novel, distinct source of forgetting specific to DETR-like architectures: background foregrounding. This arises from the exhaustiveness constraint of the Hungarian matcher, which forcibly assigns every ground truth target to one prediction, even when predictions primarily cover background regions (i.e., low IoU). This erroneous supervision compels the model to misclassify backgro...
| Authors | Qirui Wu, Shizhou Zhang, De Cheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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