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View PDF Summary:In this paper, we delve into semi-supervised item detection in which unlabeled illustrations or photos are leveraged to interrupt from the upper bound of fully-supervised item detection designs. Previous semi-supervised approaches determined by pseudo labels are seriously degenerated by sounds and vulnerable to overfit to noisy lab