Meng, Yanda 
ORCID: 0000-0001-7344-2174, Bridge, Joshua, Wei, Meng, Zhao, Yitian, Qiao, Yihong, Yang, Xiaoyun, Huang, Xiaowei and Zheng, Yalin ORCID: 0000-0002-7873-0922
(2022)
Counting with Adaptive Auxiliary Learning.
[Preprint]
|
Text
2203.04061v1.pdf - Submitted version Download (4MB) | Preview |
Abstract
This paper proposes an adaptive auxiliary task learning based approach for object counting problems. Unlike existing auxiliary task learning based methods, we develop an attention-enhanced adaptively shared backbone network to enable both task-shared and task-tailored features learning in an end-to-end manner. The network seamlessly combines standard Convolution Neural Network (CNN) and Graph Convolution Network (GCN) for feature extraction and feature reasoning among different domains of tasks. Our approach gains enriched contextual information by iteratively and hierarchically fusing the features across different task branches of the adaptive CNN backbone. The whole framework pays special attention to the objects' spatial locations and varied density levels, informed by object (or crowd) segmentation and density level segmentation auxiliary tasks. In particular, thanks to the proposed dilated contrastive density loss function, our network benefits from individual and regional context supervision in terms of pixel-independent and pixel-dependent feature learning mechanisms, along with strengthened robustness. Experiments on seven challenging multi-domain datasets demonstrate that our method achieves superior performance to the state-of-the-art auxiliary task learning based counting methods. Our code is made publicly available at: https://github.com/smallmax00/Counting_With_Adaptive_Auxiliary
| Item Type: | Preprint |
|---|---|
| Uncontrolled Keywords: | cs.CV, cs.CV |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 25 Apr 2022 13:25 |
| Last Modified: | 18 Jan 2023 21:04 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3153763 |
Share
Share
