Detect object with few examples and hidden parts by extending local to co-existing regions with ECEA
Detect object with few examples and hidden parts by extending local to co-existing regions with ECEA
ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection
arXiv paper abstract https://arxiv.org/abs/2309.08196
arXiv PDF paper https://arxiv.org/pdf/2309.08196.pdf
Few-shot object detection (FSOD) identifies objects from extremely few annotated samples ... Limited by the scarce training data ... training ... of novel classes typically capture part of objects, resulting in ... cannot detect the completely unseen object
... propose an Extensible Co-Existing Attention (ECEA) module to enable the model to infer the global object according to the local parts.
... continuously learns the extensible ability on the base stage with abundant samples and transfers it to the novel stage, which can assist the few-shot model to quickly adapt in extending local regions to co-existing regions.
... first devise an extensible attention mechanism that starts with a local region and extends attention to co-existing regions that are similar and adjacent to the given local region.
... implement the extensible attention mechanism in different feature scales to progressively discover the full object in various receptive fields.
... ECEA module can assist the few-shot detector to completely predict the object despite some regions failing to appear in the training samples and achieve the new state of the art compared with existing FSOD methods.
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