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Large-Scale Training of Shadow Detectors with Noisily-Annotated Shadow Examples | SpringerLink
Source: link.springer.com
Article summary:
1. This paper introduces a novel method for training shadow detectors using large-scale datasets with noisily-annotated shadow examples.
2. A label recovery method is proposed to automatically correct erroneous annotations, allowing the trained classifiers to perform at state-of-the-art level.
3. A semantic-aware patch level Convolutional Neural Network architecture is proposed that efficiently trains on patch level shadow examples while incorporating image level semantic information.
Article analysis:
The article provides a detailed overview of the proposed method for training shadow detectors using large-scale datasets with noisily-annotated shadow examples. The authors present a novel label recovery method which can automatically correct a portion of the erroneous annotations, allowing the trained classifiers to perform at state-of-the-art level. Furthermore, they propose a semantic-aware patch level Convolutional Neural Network architecture that efficiently trains on patch level shadow examples while incorporating image level semantic information.
The article appears to be reliable and trustworthy as it provides evidence for its claims in the form of references to existing research and experiments conducted by the authors themselves. The authors also provide an extensive discussion of related work in the field, which further adds to the credibility of their claims. Additionally, there does not appear to be any promotional content or partiality in the article, as it presents both sides equally and does not make any unsupported claims or missing points of consideration.