RD-IOD: Two-Level Residual-Distillation-Based Triple-Network for Incremental Object Detection
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Bibliographic record
Abstract
As a basic component in multimedia applications, object detectors are generally trained on a fixed set of classes that are pre-defined. However, new object classes often emerge after the models are trained in practice. Modern object detectors based on Convolutional Neural Networks (CNN) suffer from catastrophic forgetting when fine-tuning on new classes without the original training data. Therefore, it is critical to improve the incremental learning capability on object detection. In this article, we propose a novel Residual-Distillation-based Incremental learning method on Object Detection (RD-IOD). Our approach rests on the creation of a triple-network based on Faster R-CNN. To enable continuous learning from new classes, we use the original model as well as a residual model to guide the learning of the incremental model on new classes while maintaining the previous learned knowledge. To better maintain the discrimination between the features of old and new classes, the residual model is jointly trained with the incremental model on new classes in the incremental learning procedure. In addition, a two-level distillation scheme is designed to guide the training process, which consists of (1) a general distillation for imitating the original model in feature space along with a residual distillation on the features in both image level and instance level, and (2) a joint classification distillation on the output layers. To well preserve the learned knowledge, we design a 2-threshold training strategy to guide the learning of a Region Proposal Network and a detection head. Extensive experiments conducted on VOC2007 and COCO demonstrate that the proposed method can effectively learn to incrementally detect objects of new classes, and the problem of catastrophic forgetting is mitigated. Our code is available at https://github.com/yangdb/RD-IOD.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it