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Abstract

Systems and methods for visual object detection using explicit negatives. To train an artificial intelligence model with explicit negatives, a data sampler can sample input data from a language-based dataset to select images with annotations. A negative generation engine can generate explicit negatives representing sentences that include contradicting words that are semantically related to the annotations by using an external knowledgebase. A model trainer can minimize the classification loss of positive labels while decreasing the confidence score of the explicit negatives for the artificial intelligence model. The negative generation engine can be optimized to generate next explicit negatives. The artificial intelligence model can backpropagate using positive labels and the next explicit negatives to generate supervisory loss corresponding to the net explicit negatives. The artificial intelligence model can detect objects from an input image.

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