Category‐wise feature extractor based on ADL method for weak‐supervised object localisation. Issue 15 (16th February 2021)

• Record Type: Journal Article
• Authors: Hu, Yanzhu; Zhu, Dongdong; Ai, Xinbo; Xu, Yabo
• Is Part Of: IET image processing. Volume 14:Issue 15(2020)
• Page Start: 3965
• Page End: 3974
• Publication Date: 2021-02-16
• Subjects: object detection -- feature extraction -- learning (artificial intelligence) -- image classification -- supervised learning; category output -- object location -- localisation map -- attention‐based dropout layer method -- localisation task -- classification task -- weak supervised object localisation -- location annotation -- object category annotation -- supervised object detection -- weak‐supervised object localisation -- category‐wise feature extractor -- ADL method -- lightweight CFE‐tiny method -- standard CFE method; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.0640
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Fully supervised object detection needs to use the data set with object category annotation and location annotation to train the model. In contrast, weak supervised object localisation only needs to use the data set with object category annotation to train the model, but it can complete the classification task and object localisation task at the same time. Inspired by the attention‐based dropout layer (ADL) method, this study designs a category‐wise feature extractor (CFE), which can explicitly obtain the localisation map used to indicate the object location, and it is directly related to the category output of the classification task. Although its amount of calculation is slightly larger than that of the ADL method, the performance is better than ADL in some tasks. In addition to the standard CFE method mentioned above, this study also designs a lightweight CFE‐tiny method, which adopts split‐attention mechanism, and the calculation amount of this method is much smaller than that of ADL method.