Multi‐label Image Classification via Coarse‐to‐Fine Attention*. Issue 6 (1st November 2019)

• Record Type: Journal Article
• Authors: Lyu, Fan; Li, Linyan; Victor, S. Sheng; Fu, Qiming; Hu, Fuyuan
• Is Part Of: Chinese journal of electronics. Volume 28:Issue 6(2019)
• Page Start: 1118
• Page End: 1126
• Publication Date: 2019-11-01
• Subjects: Multi‐label classification -- Convolutional neural network -- Recurrent neural network -- Attention; feature extraction -- image classification -- image recognition -- image representation -- learning (artificial intelligence) -- neural nets -- object detection -- visual databases; attention mechanism -- conventional attention‐based methods -- positive labels -- negative labels -- image classification method -- popular multilabel image datasets -- multilabel image classification -- coarse‐to‐fine attention; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2019.07.015
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : Great efforts have been made by using deep neural networks to recognize multi‐label images. Since multi‐label image classification is very complicated, many studies seek to use the attention mechanism as a kind of guidance. Conventional attention‐based methods always analyzed images directly and aggressively, which is difficult to well understand complicated scenes. We propose a global/local attention method that can recognize a multi‐label image from coarse to fine by mimicking how human‐beings observe images. Our global/local attention method first concentrates on the whole image, and then focuses on its local specific objects. We also propose a joint max‐margin objective function, which enforces that the minimum score of positive labels should be larger than the maximum score of negative labels horizontally and vertically. This function further improve our multi‐label image classification method. We evaluate the effectiveness of our method on two popular multi‐label image datasets ( i.e., Pascal VOC and MS‐COCO). Our experimental results show that our method outperforms state‐of‐the‐art methods.