S2-Boost: Synergistic Semantic Boosting for Coarse-to-Fine Ensemble Learning
Guanxiong He, Zheng Wang, Jie Wang, Liaoyuan Tang, Rong Wang, Feiping Nie
Abstract
Neuroscientific evidence reveals that human visual recognition is not an instantaneous event but a hierarchical process, where the brain constructs a holistic perception by progressively integrating simple features like edges or texture into complex scenes. Ensemble learning successfully utilizes this principle, yet existing methods typically integrate models at the decision level, neglecting the rich, complementary information within the feature space itself and thus fundamentally limiting their potential. To address this, we introduce Synergistic Semantic Boosting (S2-Boosting), a framework that employs a self-supervised hierarchical semantic learning module to decompose an image into complementary, semantically meaningful parts autonomously. These parts guide a boosting procedure where a sequence of specialized learners, each focusing on a specific semantic partition, collaboratively corrects the ensemble
BibTeX
@inproceedings{aaai2026_s2boostsynergist,
title = {S2-Boost: Synergistic Semantic Boosting for Coarse-to-Fine Ensemble Learning},
author = {Guanxiong He and Zheng Wang and Jie Wang and Liaoyuan Tang and Rong Wang and Feiping Nie},
booktitle = {AAAI 2026},
year = {2026}
}