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Keyword: positive and negative sample selection ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection

   Zhang, Chi, Yao, Lei and Li · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2020

   The paper argues that how training samples are labelled positive or negative explains the gap between anchor-based and anchor-free detectors, and proposes ATSS, an adaptive selection method, to improve both.

   Unchecked2 claims
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   1. UncheckedWhen anchor-based and anchor-free detectors define positive and negative training samples the same way, their final performance shows no obvious difference.“If they adopt the same definition of positive and negative samples during training, there is no obvious difference in the final performance, no matter regressing from a box or a point.”
   2. Unchecked“With the newly introduced ATSS, we improve state-of-the-art detectors by a large margin to $50.7\%$ AP without introducing any overhead.”