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IGCDet: Independence guided co-training for sparsely annotated object detection
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AI
DOI：10.1016/j.knosys.2025.115217.png)
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Abstract
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Object detection models can achieve excellent detection performance with fully annotated instances. However, requiring complete annotations for every dataset is impractical due to high labor and time costs, as well as the inevitable occurrence of missing annotations. As a result, the absence of annotations can potentially provide misleading supervision and harm the training process. Recent methodologies have achieved remarkable effectiveness through the application of Co-Mining. However, the independence of each branch in Co-Mining cannot be guaranteed, overlooking valuable information during multi-perspective training. To address this issue, we introduce an Independence Guided Co-Training Model (IGCDet) that leverages Image Independence Decomposition to ensure the independence of each co-training branch. This model aims to capture diverse perspectives from images as extensively as possible, identifying missing annotations and incorporating them as positive supervision in the training process. Additionally, we propose the use of Joint-Confidence, derived from the combination of classification and regression, as pseudo-label scores, effectively mitigating issues associated with pseudo-label bias. Extensive experiments have verified the effectiveness of the proposed method.
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