.png){kind=icon}
Return
Deeply scale aggregation network for object counting
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2020.106485.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Object counting is a fundamental and essential task to build the high-level cognition for the given scene in the computer vision field. For the common scale variant issue in the object counting, this paper designs a deeply scale aggregation network for object counting task. Specially, we design a dense multi-scale (DMS) block to extract the initial scale-aware feature, and thus stack multiple DMS blocks in the specific structure to implement the adaptive network depth, so as to learn the more mighty scale-aware feature. Extensive experimental results are reported in this paper, using up to six public object counting benchmarks, which demonstrate that the proposed method has an effective performance for object counting task. And the ablation studies validate the structural rationality of the proposed method. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Object counting
Multi-scale analysis
Scale variant
Scale aggregation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
Generation of hydroxyl radicals by urban suspended particulate air matter. The role of iron ions
PREAI
ShareSave
Recurrent reverse attention guided residual learning for saliency object detection.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSaveShareSave
View more