Semi-ViM: Bidirectional State Space Model for Mitigating Label Imbalance in Semi-Supervised Learning
Hongyang He, Hongyang Xie, Haochen You, Victor Sanchez
Abstract
Semi-supervised learning (SSL) is often hindered by learning biases when imbalanced datasets are used for training, which limits its effectiveness in real-world applications. In this paper, we propose Semi-ViM, a novel SSL framework based on Vision Mamba, a bidirectional state space model (SSM) that serves as a superior alternative to Transformer-based architectures for visual representation learning. Semi-ViM effectively deals with label imbalance and improves model stability through two key innovations: LyapEMA, a stability-aware parameter update mechanism inspired by Lyapunov theory, and SSMixup, a novel mixup strategy applied at the hidden state level of bidirectional SSMs. Experimental results on ImageNet-1K and ImageNet-LT demonstrate that Semi-ViM significantly outperforms state-of-the-art SSL models, achieving 85.40% accuracy with only 10% of the labeled data, surpassing Transformer-based methods such as Semi-ViT.
BibTeX
@InProceedings{He_2025_ICCV,
author = {He, Hongyang and Xie, Hongyang and You, Haochen and Sanchez, Victor},
title = {Semi-ViM: Bidirectional State Space Model for Mitigating Label Imbalance in Semi-Supervised Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {765-774}
}