A weakly supervised object detection method based on attention mechanism

Hanchao Liu; Diancheng Zhou; Yang Zhao; Lanfang Dong

doi:10.1117/12.2623134

16 February 2022 A weakly supervised object detection method based on attention mechanism

Hanchao Liu, Diancheng Zhou, Yang Zhao, Lanfang Dong

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120832K (2022) https://doi.org/10.1117/12.2623134
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

Object detection is one of the basic problems in computer vision. Currently, the detection models based on fully supervised learning which demand fine labeled data such as bounding box annotated images are the mainstream of this research field. However, obtaining high-precision tagged images usually costs huge time and human labor. To lighten the restriction for training data of detection models, we propose an attention-based weakly supervised object detection model which can be trained only using image-level annotated images. The weakly supervised object detection model consists of two stages. In the first stage, an attention-based convolutional neural network (CNN) is designed to enhance the localization ability of CNN and generate coarse detection results. In the second stage, a neural network for edge detection is utilized to get the fine results based on the coarse results in stage one. Tested on PascalVOC 2007 and 2012, the proposed weakly supervised learning detection model achieves 53.4mAP and 48.9mAP in these two datasets, respectively, which is competitive with the state-of-the-art weakly supervised learning detection models and reduces the gap with the fully supervised learning detection models.

Citation Download Citation

Hanchao Liu, Diancheng Zhou, Yang Zhao, and Lanfang Dong "A weakly supervised object detection method based on attention mechanism", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120832K (16 February 2022); https://doi.org/10.1117/12.2623134

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Edge detection

Machine learning

Target detection

Performance modeling

Visual process modeling

Detection and tracking algorithms

Show All Keywords

Keywords/Phrases

Search In:

Publication Years