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arXiv · 2410.10589

MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge Transfer

Minghao Zhu·Zhengpu Wang·Mengxian Hu·Ronghao Dang·Xiao Lin·Xun Zhou·Chengju Liu·Qijun Chen
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Abstract

Transferring visual-language knowledge from large-scale foundation models for video recognition has proved to be effective. To bridge the domain gap, additional parametric modules are added to capture the temporal information. However, zero-shot generalization diminishes with the increase in the number of specialized parameters, making existing works a trade-off between zero-shot and close-set performance. In this paper, we present MoTE, a novel framework that enables generalization and specialization to be balanced in one unified model. Our approach tunes a mixture of temporal experts to learn multiple task views with various degrees of data fitting. To maximally preserve the knowledge of each expert, we propose \emph{Weight Merging Regularization}, which regularizes the merging process of experts in weight space. Additionally with temporal feature modulation to regularize the contribution of temporal feature during test. We achieve a sound balance between zero-shot and close-set video recognition tasks and obtain state-of-the-art or competitive results on various datasets, including Kinetics-400 \& 600, UCF, and HMDB. Code is available at \url{https://github.com/ZMHH-H/MoTE}.

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Minghao Zhu, Zhengpu Wang, Mengxian Hu, Ronghao Dang, Xiao Lin, Xun Zhou, Chengju Liu, Qijun Chen. 2024-10-14. MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge Transfer. https://arxiv.org/abs/2410.10589

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