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Dilated-Scale-Aware Category-Attention ConvNet for Multi-Class Object Counting
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DOI：10.1109/LSP.2021.3096119.png)
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摘要
En 中文
Object counting aims to estimate the number of objects in images. The leading counting approaches focus on single-category counting tasks and achieve impressive performance. Nevertheless, there are multiple categories of objects in real scenes. Multi-class object counting expands the scope of application of object counting tasks. The multi-target detection task can achieve multi-class object counting in some scenarios. However, it requires the dataset annotated with bounding boxes. Compared with the point-level annotations used in mainstream object counting issues, the box-level annotations are more difficult to be obtained. In this paper, we propose a simple yet efficient counting network based on point-level annotations. Specifically, we first change the traditional estimated density map from one to the number of categories to achieve multi-class object counting. Since all categories of objects use the same feature extractor, their features will interfere mutually in the shared feature space. We further design a multi-mask structure to suppress the negative interaction among objects. Extensive experiments on the challenging benchmarks demonstrate that the proposed method achieves state-of-the-art counting performance. The code is available at https://github.com/PRIS-CV/DSACA.
Keyword:
Annotations
Feature extraction
Task analysis
Convolution
Automobiles
Training
Visualization
Multi-class object counting
point-level annotation
dilated-scale-aware module
category-attention module
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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引用论文
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NATURE METHODS
NATURE METHODSIF32.1
PREAI
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