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Paper Title
Saliency-Based Image Classification: A Survey of Methods, Applications, and Open Challenges
Authors
Mehjabin Saiyad , Meet Trivedi , Preet Prajapati , Jigar Dalvadi
Keywords
saliency maps; image classification; vision transformers; explainable AI; saliency-guided training; data augmentation; token pruning; medical imaging
Abstract
Saliency, the identification of the image regions that matter most for a visual decision, has evolved from a hand-crafted model of human attention into a general-purpose signal that is used to supervise, augment, compress, and explain modern image classifiers. This survey reviews recent work from 2024 to 2026 on saliency-based image classification and organises it into six categories: (A) saliency-guided data augmentation and mixup, (B) saliency-guided training objectives, (C) saliency-guided input and token selection for efficient inference, (D) saliency-guided feature mining, (E) saliency as explanation and its evaluation, and (F) robustness, trust, and security. For each category we summarise representative methods, the reported empirical gains, and the assumptions on which these gains rest. Across studies, we observe that (i) human or human-like saliency is a useful training signal when labelled data are scarce, (ii) Vision Transformer (ViT) attention has become the dominant saliency source for efficiency-oriented methods, and (iii) the faithfulness of saliency maps remains contested, which limits their use as clinical or safety evidence. We close with a discussion of open problems, including standardised evaluation, the cost of collecting human saliency, and the interaction between saliency and robustness. All quantitative results are reported as stated by the original authors and are not directly comparable across studies.
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JMVRA — journal style
"Saliency-Based Image Classification: A Survey of Methods, Applications, and Open Challenges", JMVRA - Journal of Modern and Versatile Research Approach (www.jmvra.org), ISSN:2984-8911, Vol.4, Issue 10, page no.a111-a118, October-2026, Available :https://jmvra.org/papers/JMVRA2610011.pdf
APA — 7th edition
Saiyad, M., Trivedi, M., Prajapati, P., & Dalvadi, J. (2026). Saliency-Based Image Classification: A Survey of Methods, Applications, and Open Challenges. Journal of Modern and Versatile Research Approach, 4(10), a111-a118. https://jmvra.org/viewpaperforall.php?paper=JMVRA2610011
MLA — 9th edition
Saiyad, Mehjabin, et al. "Saliency-Based Image Classification: A Survey of Methods, Applications, and Open Challenges." Journal of Modern and Versatile Research Approach, vol. 4, no. 10, 2026, pp. a111-a118, https://jmvra.org/viewpaperforall.php?paper=JMVRA2610011.
Chicago — 17th, bibliography
Saiyad, Mehjabin, et al. "Saliency-Based Image Classification: A Survey of Methods, Applications, and Open Challenges." Journal of Modern and Versatile Research Approach 4, no. 10 (2026): a111-a118. https://jmvra.org/viewpaperforall.php?paper=JMVRA2610011.
Harvard — author–date
Saiyad, M. et al. (2026) 'Saliency-Based Image Classification: A Survey of Methods, Applications, and Open Challenges', Journal of Modern and Versatile Research Approach, 4(10), pp. a111-a118. Available at: https://jmvra.org/viewpaperforall.php?paper=JMVRA2610011
IEEE — numbered reference
M. Saiyad, M. Trivedi, P. Prajapati and J. Dalvadi, "Saliency-Based Image Classification: A Survey of Methods, Applications, and Open Challenges," JMVRA, vol. 4, no. 10, pp. a111-a118, Oct. 2026.
Vancouver — biomedical
Saiyad M, Trivedi M, Prajapati P, Dalvadi J. Saliency-Based Image Classification: A Survey of Methods, Applications, and Open Challenges. JMVRA. 2026 Oct;4(10):a111-a118.
AMA — 11th edition
Saiyad M, Trivedi M, Prajapati P, Dalvadi J. Saliency-Based Image Classification: A Survey of Methods, Applications, and Open Challenges. JMVRA. 2026;4(10):a111-a118. Accessed at https://jmvra.org/viewpaperforall.php?paper=JMVRA2610011
BibTeX — Zotero, Mendeley, LaTeX
@article{JMVRA2610011,
author = {Mehjabin Saiyad and Meet Trivedi and Preet Prajapati and Jigar Dalvadi},
title = {Saliency-Based Image Classification: A Survey of Methods, Applications, and Open Challenges},
journal = {Journal of Modern and Versatile Research Approach},
volume = {4},
number = {10},
pages = {a111-a118},
year = {2026},
month = {October},
issn = {2984-8911},
url = {https://jmvra.org/viewpaperforall.php?paper=JMVRA2610011}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Saiyad, Mehjabin
AU - Trivedi, Meet
AU - Prajapati, Preet
AU - Dalvadi, Jigar
TI - Saliency-Based Image Classification: A Survey of Methods, Applications, and Open Challenges
T2 - Journal of Modern and Versatile Research Approach
JA - JMVRA
VL - 4
IS - 10
PY - 2026
SN - 2984-8911
UR - https://jmvra.org/viewpaperforall.php?paper=JMVRA2610011
SP - a111-a118
ER -
Issue
Volume 4 Issue 10,
October-2026
Pages : a111-a118
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- https://jmvra.org/papers/JMVRA2610011.pdf
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