Open AccessWeakly supervised object detection based on deep metric learning
Author(s) -
Weiming Dong,
Xiaoming Zhang,
Ping Zhao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1748/2/022015
Subject(s) - pascal (unit) , artificial intelligence , computer science , metric (unit) , object detection , supervised learning , similarity (geometry) , annotation , object (grammar) , pattern recognition (psychology) , machine learning , proxy (statistics) , similarity measure , computer vision , image (mathematics) , artificial neural network , economics , programming language , operations management
Weakly supervised object detection is a hot issue in the computer vision field, which aims to train a high performance detection model with low cost annotation data. The existing methods of weakly supervised object detection only summarize the object category but don’t consider the similarity between the objects in the optimization process. For solving this problem to improve detection accuracy, this paper proposes a weakly supervised object detection model based on deep metric learning. In the initial training phase, an initial metric has been learned in advance to measure the similarity between these objects; in the correction phase, we propose an adjacent instance mining method based on proxy samples, this approach expands the model’s recognition view, and prevents premature locking the wrong object position. We design a series of experiments on the PASCAL VOC2007 dataset to prove the effectiveness of this method.