Modality-Balanced Collaborative Distillation for Multi-Modal Domain Generalization
Xiaohan Wang, Zhangtao Cheng, Ting Zhong, Leiting Chen, Fan Zhou
Abstract
Weight Averaging (WA) has emerged as a powerful technique for enhancing generalization by promoting convergence to a flat loss landscape, which correlates with stronger out-of-distribution performance. However, applying WA directly to multi-modal domain generalization (MMDG) is challenging: differences in optimization speed across modalities lead WA to overfit to faster-converging ones in early stages, suppressing the contribution of slower yet complementary modalities, thereby hindering effective modality fusion and skewing the loss surface toward sharper, less generalizable minima. To address this issue, we propose MBCD, a unified collaborative distillation framework that retains WA
BibTeX
@inproceedings{aaai2026_modalitybalanced,
title = {Modality-Balanced Collaborative Distillation for Multi-Modal Domain Generalization},
author = {Xiaohan Wang and Zhangtao Cheng and Ting Zhong and Leiting Chen and Fan Zhou},
booktitle = {AAAI 2026},
year = {2026}
}