Publications

@article{gangwar_deephsar_2024,

title = {DeepHSAR: Semi-supervised fine-grained learning for multi-label human sexual activity recognition},

author = {Abhishek Gangwar and Víctor González-Castro and Enrique Alegre and Eduardo Fidalgo and Alicia Martínez-Mendoza},

url = {https://www.sciencedirect.com/science/article/pii/S0306457324001596},

year  = {2024},

date = {2024-01-01},

journal = {Information Processing & Management},

volume = {61},

number = {5},

pages = {103800},

abstract = {This paper presents DeepHSAR, a deep learning framework for semi-supervised Human Sexual Activity Recognition (HSAR), using the SexualActs-150k dataset with 150k images. It employs two classification streams for global and fine-grained recognition, achieving an F1-score of 79.29%. The method outperforms previous approaches and achieves 99.85% accuracy on the NPDI Pornography-2k dataset.},

note = {Publisher: Pergamon},

keywords = {Fine-grained Classification, Multi-label Classification, pornography detection, Semi-supervised Classification, Sexual Activity Detection},

pubstate = {published},

tppubtype = {article}

}