Crowd counting is a difficult task because of the diversity of scenes. Most of the existing crowd counting methods adopt complex structures with massive backbones to enhance the generalization ability. Unfortunately, the performance of existing methods on large-scale data sets is not satisfactory. In order to handle various scenarios with less complex network, we explored how to efficiently use the multi-expert model for crowd counting tasks. We mainly focus on how to train more efficient expert networks and how to choose the most suitable expert. Specifically, we propose a task-driven similarity metric based on sample’s mutual enhancement, referred as co-fine-tune similarity, which can find a more efficient subset of data for training the expert network. Similar samples are considered as a cluster which is used to obtain parameters of an expert. Besides, to make better use of the proposed method, we design a simple network called FPN with Deconvolution Counting Network, which is a more suitable base model for the multi-expert counting network. Experimental results show that multiple experts FDC (MFDC) achieves the best performance on four public data sets, including the large scale NWPU-Crowd data set. Furthermore, the MFDC trained on an extensive dense crowd data set can generalize well on the other data sets without extra training or fine-tuning.

Exploiting sample correlation for crowd counting with multi-expert network / Liu, Xinyan; Li, Guorong; Han, Zhenjun; Zhang, Weigang; Yang, Yifan; Huang, Qingming; Sebe, Nicu. - (2021), pp. 3195-3204. (Intervento presentato al convegno ICCV’21 tenutosi a Virtual event nel 11th-17th October 2021) [10.1109/ICCV48922.2021.00320].

Exploiting sample correlation for crowd counting with multi-expert network

Sebe, Nicu
2021-01-01

Abstract

Crowd counting is a difficult task because of the diversity of scenes. Most of the existing crowd counting methods adopt complex structures with massive backbones to enhance the generalization ability. Unfortunately, the performance of existing methods on large-scale data sets is not satisfactory. In order to handle various scenarios with less complex network, we explored how to efficiently use the multi-expert model for crowd counting tasks. We mainly focus on how to train more efficient expert networks and how to choose the most suitable expert. Specifically, we propose a task-driven similarity metric based on sample’s mutual enhancement, referred as co-fine-tune similarity, which can find a more efficient subset of data for training the expert network. Similar samples are considered as a cluster which is used to obtain parameters of an expert. Besides, to make better use of the proposed method, we design a simple network called FPN with Deconvolution Counting Network, which is a more suitable base model for the multi-expert counting network. Experimental results show that multiple experts FDC (MFDC) achieves the best performance on four public data sets, including the large scale NWPU-Crowd data set. Furthermore, the MFDC trained on an extensive dense crowd data set can generalize well on the other data sets without extra training or fine-tuning.
2021
Proceedings: 2021 IEEE/CVF International Conference on Computer Vision
Piscataway, NJ
IEEE
978-1-6654-2812-5
Liu, Xinyan; Li, Guorong; Han, Zhenjun; Zhang, Weigang; Yang, Yifan; Huang, Qingming; Sebe, Nicu
Exploiting sample correlation for crowd counting with multi-expert network / Liu, Xinyan; Li, Guorong; Han, Zhenjun; Zhang, Weigang; Yang, Yifan; Huang, Qingming; Sebe, Nicu. - (2021), pp. 3195-3204. (Intervento presentato al convegno ICCV’21 tenutosi a Virtual event nel 11th-17th October 2021) [10.1109/ICCV48922.2021.00320].
File in questo prodotto:
File Dimensione Formato  
Liu_Exploiting_Sample_Correlation_for_Crowd_Counting_With_Multi-Expert_Network_ICCV_2021_paper.pdf

accesso aperto

Tipologia: Post-print referato (Refereed author’s manuscript)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.8 MB
Formato Adobe PDF
1.8 MB Adobe PDF Visualizza/Apri
Exploiting_sample_correlation_for_crowd_counting_with_multi-expert_network.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.45 MB
Formato Adobe PDF
2.45 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/326208
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 20
  • ???jsp.display-item.citation.isi??? 7
social impact