@inproceedings{
he2024robust,
title={Robust Variational Contrastive Learning for Partially View-unaligned Clustering},
author={Changhao He and Hongyuan Zhu and Peng Hu and Xi Peng},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=eZpm234cw2}
}
Although multi-view learning has achieved remarkable progress over the past decades, most existing methods implicitly assume that all views (or modalities) are well-aligned. In practice, however, collecting fully aligned views is challenging due to complexities and discordances in time and space, resulting in the Partially View-unaligned Problem (PVP), such as audio-video asynchrony caused by network congestion. While some methods are proposed to align the unaligned views by learning view-invariant representations, almost all of them overlook specific information across different views for complementarity, limiting performance improvement. To address these problems, we propose a robust framework, dubbed \textbf{V}ariat\textbf{I}onal Con\textbf{T}r\textbf{A}stive \textbf{L}earning (VITAL), designed to learn both common and specific information simultaneously. To be specific, each data sample is first modeled as a Gaussian distribution in the latent space, where the mean estimates the most probable common information, while the variance indicates view-specific information. Second, by using variational inference, VITAL conducts intra- and inter-view contrastive learning to preserve common and specific semantics in the distribution representations, thereby achieving comprehensive perception. As a result, the common representation (mean) could be used to guide category-level realignment, while the specific representation (variance) complements sample semantic information, thereby boosting overall performance. Finally, considering the abundance of False Negative Pairs (FNPs) generated by unsupervised contrastive learning, we propose a robust loss function that seamlessly incorporates FNP rectification into the contrastive learning paradigm. Empirical evaluations on eight benchmark datasets reveal that VITAL outperforms ten state-of-the-art deep clustering baselines, demonstrating its efficacy in both partially and fully aligned scenarios. The Code is available at \url{https://github.com/He-Changhao/2024-MM-VITAL}.
Robust Variational Contrastive Learning for Partially View-unaligned Clustering
Changhao He Hongyuan Zhu Peng Hu Xi Peng