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Multiple papers on learning with limited supervision

Riverside, Ca –

VCG researchers have recently published a number of papers on learning in computer vision with limited supervision. Our paper in T-PAMI proposes a method for active learning by exploiting contextual data. The ECCV-18 work presents a framework for localizing activities in videos using weak supervision during training and our CVPR-19 paper shows how weak supervision can be used for video moment retrieval using text descriptions. In the T-IP paper, we show that information theoretic typicality can be exploited for identifying a minimal subset for manual labeling, while our paper in T-CSVT explored dataset creation with active learning. Below are links to the papers.