RD-IOD: Two-Level Residual-Distillation-Based Triple-Network for Incremental Object Detection
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".