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Representation Learning with Adaptive Superpixel Coding

by Mahmoud Khalil, Ahmad Khalil, Alioune Ngom
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 62
Year of Publication: 2025
Authors: Mahmoud Khalil, Ahmad Khalil, Alioune Ngom
10.5120/ijca2025926002

Mahmoud Khalil, Ahmad Khalil, Alioune Ngom . Representation Learning with Adaptive Superpixel Coding. International Journal of Computer Applications. 187, 62 ( Dec 2025), 1-17. DOI=10.5120/ijca2025926002

@article{ 10.5120/ijca2025926002,
author = { Mahmoud Khalil, Ahmad Khalil, Alioune Ngom },
title = { Representation Learning with Adaptive Superpixel Coding },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2025 },
volume = { 187 },
number = { 62 },
month = { Dec },
year = { 2025 },
issn = { 0975-8887 },
pages = { 1-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number62/representation-learning-with-adaptive-superpixel-coding/ },
doi = { 10.5120/ijca2025926002 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-12-18T17:49:51+05:30
%A Mahmoud Khalil
%A Ahmad Khalil
%A Alioune Ngom
%T Representation Learning with Adaptive Superpixel Coding
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 62
%P 1-17
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Deep learning vision models are typically tailored for specific modalities and often rely on domain-specific assumptions, such as the grid structures used by most existing architectures. This paper introduces a self-supervised Transformer-based model called Adaptive Superpixel Coding (ASC). The key idea behind the approach is to address the limitations of traditional Vision Transformers, which depend on fixed-size and non-adaptive patch partitioning. Instead, ASC employs adaptive superpixel layers that dynamically adjust to the underlying image content. The study analyzes the properties that make the proposed method effective and demonstrates that the approach outperforms widely used baselines on standard image downstream task benchmarks.

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Index Terms

Computer Science
Information Sciences

Keywords

Self-Supervised Learning Transformers Superpixels Adaptive Patchification Vision Models Deep Learning

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