Learn new objects without forgetting old ones by learning causal features with ICOD
Learn new objects without forgetting old ones by learning causal features with ICOD
Learning Causal Features for Incremental Object Detection
arXiv paper abstract https://arxiv.org/abs/2403.00591
arXiv PDF paper https://arxiv.org/pdf/2403.00591.pdf
... propose an incremental causal object detection (ICOD) model by learning causal features, which can adapt to more tasks.
Traditional object detection models, unavoidably depend on the data-bias or data-specific features to get the detection results, which can not adapt to the new task.
When the model meets the requirements of incremental learning, the data-bias information
is not beneficial to the new task, and the incremental learning may eliminate these features and lead to forgetting.
... ICOD is introduced to learn the causal features, rather than the data-bias features when training the detector.
Thus, when the model is implemented to a new task, the causal features of the old task can aid the incremental learning process to alleviate the catastrophic forgetting problem.
... shows a causal feature without data-bias can make the model adapt to new tasks better ...
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