CCF-Net: A Cascade Center-Based Framework Towards Efficient Human Parts Detection

Kai Ye, Haoqin Ji, Yuan Li, Lei Wang, Peng Liu, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review


Human parts detection has made remarkable progress due to the development of deep convolutional networks. However, many SOTA detection methods require large computational cost and are still difficult to be deployed to edge devices with limited computing resources. In this paper, we propose a lightweight Cascade Center-based Framework, called CCF-Net, for human parts detection. Firstly, a Gaussian-Induced penalty strategy is designed to ensure that the network can handle objects of various scales. Then, we use Cascade Attention Module to capture relations between different feature maps, which refines intermediate features. With our novel cross-dataset training strategy, our framework fully explores the datasets with incomplete annotations and achieves better performance. Furthermore, Center-based Knowledge Distillation is proposed to enable student models to learn better representation without additional cost. Experiments show that our method achieves a new SOTA performance on Human-Parts and COCO Human Parts benchmarks(The Datasets used in this paper were downloaded and experimented on by Kai Ye from Shenzhen University.).

Original languageEnglish
Title of host publicationMultiMedia Modeling - 29th International Conference, MMM 2023, Proceedings
EditorsDuc-Tien Dang-Nguyen, Cathal Gurrin, Alan F. Smeaton, Martha Larson, Stevan Rudinac, Minh-Son Dao, Christoph Trattner, Phoebe Chen
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783031278174
Publication statusPublished - 2023
Externally publishedYes
Event29th International Conference on MultiMedia Modeling, MMM 2023 - Bergen, Norway
Duration: 9 Jan 202312 Jan 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13834 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference29th International Conference on MultiMedia Modeling, MMM 2023


  • Human parts
  • Knowledge distillation
  • Object detection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


Dive into the research topics of 'CCF-Net: A Cascade Center-Based Framework Towards Efficient Human Parts Detection'. Together they form a unique fingerprint.

Cite this