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Task-decoupled interactive embedding network for object detection
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AI
DOI：10.1007/s10994-023-06430-w.png)
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Abstract
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Traditional object detection methods rely on manually annotated data, which can be costly and time-consuming, particularly for objects with low occurrence frequency or those that are neglected in existing datasets. When we need to generalize the model from the training datasets to the target datasets, false positive detection will appear with limited annotations in some categories and the model performance will decrease for unseen categories. In this paper, we found that the problems are related to the model's overfitting to foreground objects during the training stage and the inadequate robustness of feature representations. In order to effectively improve generalization of deep learning network, we propose a task-decoupled interactive embedding network. We decouple the sub-tasks in the detection pipeline with parallel convolution branches, with gradient propagation independently and anchor boxes generation from coarse to fine. And we introduce an embedding-interactive self-supervised decoder into the detector, so that the weaker object representations can be enhanced, and the representations of the same object can be closely aggregated, providing multi-scale semantic information for detection. Our method achieves great results on two visual tasks: few-shot object detection and open world object detection. It can effectively improve generalization on novel classes without hurting the detection of base classes and have good generalization ability for unknown categories detection. Our code is available at: https://github.com/hommelibrelm/DINet.
Keywords:
Object detection
Transformer
Task-decoupled network
Interactive embedding network
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