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Found 2 papers, 1 papers with code
Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection
However, a deep understanding of how AP loss affects the detector from a pairwise ranking perspective has not yet been developed. In this work, we revisit the average precision (AP)loss and reveal that the crucial element is that of selecting the ranking pairs between positive and negative samples. Based on this observation, we propose two strategies to improve the AP loss.
Adaptive Transfer Learning of Multi-View Time Series Classification
In this paper, we first proposed a general adaptive transfer learning framework for multi-view time series data, which shows strong ability in storing inter-view importance value in the process of knowledge transfer.
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