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Related Experiment Video

Updated: Jan 9, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

End-to-end human parsing and detection optimized for resource-constrained devices.

Md Imran Hosen^1,2, Tarkan Aydin³, Md Baharul Islam^3,4,5

¹Department of Computer Engineering, Bahcesehir University, Yildiz, Ciragan Cd, Besiktas, 34349, Istanbul, Turkey. mdimran.hosen@bau.edu.tr.

Scientific Reports

|December 10, 2025

Summary

We developed an efficient human parsing framework using self-attention for better contextual understanding. This method is optimized for low-resource devices, achieving fast and accurate results in a single pass.

Keywords:

Multi-human parsing Polygons annotation Resource-constrained devices Self-attention

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Last Updated: Jan 9, 2026

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Human parsing is crucial for human-centric analysis, segmenting clothing and body parts.
Current methods often require auxiliary inputs, hindering use on resource-constrained devices.

Purpose of the Study:

To propose an end-to-end framework for human parsing optimized for low-resource environments.
To enhance contextual understanding and enable simultaneous detection and parsing.

Main Methods:

Implemented a transformer-based self-attention module for improved contextual information.
Introduced bounding-polygon annotations for integrated detection and parsing.
Optimized the framework for efficient inference on resource-limited hardware.

Main Results:

Achieved fine-grained human parsing results in a single inference pass.
Significantly improved inference speed without compromising accuracy.
Demonstrated effectiveness and efficiency on a Raspberry Pi for real-world validation.

Conclusions:

The proposed framework offers an efficient and accurate solution for human parsing in resource-constrained settings.
The integration of self-attention and bounding-polygon annotations advances single-pass human parsing capabilities.