UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Dual-attention Focused Module for Weakly Supervised Object Localization

Zhou, Y; Chen, Z; Shen, H; Liu, Q; Zhao, R; Liang, Y; (2019) Dual-attention Focused Module for Weakly Supervised Object Localization. ArXiv Green open access

[thumbnail of 1909.04813v1.pdf]
Preview
Text
1909.04813v1.pdf - Accepted Version

Download (737kB) | Preview

Abstract

The research on recognizing the most discriminative regions provides referential information for weakly supervised object localization with only image-level annotations. However, the most discriminative regions usually conceal the other parts of the object, thereby impeding entire object recognition and localization. To tackle this problem, the Dual-attention Focused Module (DFM) is proposed to enhance object localization performance. Specifically, we present a dual attention module for information fusion, consisting of a position branch and a channel one. In each branch, the input feature map is deduced into an enhancement map and a mask map, thereby highlighting the most discriminative parts or hiding them. For the position mask map, we introduce a focused matrix to enhance it, which utilizes the principle that the pixels of an object are continuous. Between these two branches, the enhancement map is integrated with the mask map, aiming at partially compensating the lost information and diversifies the features. With the dual-attention module and focused matrix, the entire object region could be precisely recognized with implicit information. We demonstrate outperforming results of DFM in experiments. In particular, DFM achieves state-of-the-art performance in localization accuracy in ILSVRC 2016 and CUB-200-2011.

Type: Working / discussion paper
Title: Dual-attention Focused Module for Weakly Supervised Object Localization
Open access status: An open access version is available from UCL Discovery
Publisher version: https://doi.org/10.48550/arXiv.1909.04813
Language: English
Additional information: This work is licensed under an Attribution 4.0 International License (CC BY 4.0).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10137826
Downloads since deposit
Loading...
15Downloads
Download activity - last month
Loading...
Download activity - last 12 months
Loading...
Downloads by country - last 12 months
1.Turkey
1
2.Russian Federation
1

Archive Staff Only

View Item View Item