Hierarchical Banzhaf Interaction for General Video-Language Representation Learning

• URL: http://arxiv.org/abs/2412.20964v1
• Date: Mon, 30 Dec 2024 14:09:15 GMT
• Title: Hierarchical Banzhaf Interaction for General Video-Language Representation Learning
• Authors: Peng Jin, Hao Li, Li Yuan, Shuicheng Yan, Jie Chen,
• Abstract summary: Multimodal representation learning plays an important role in the artificial intelligence domain. We introduce a new approach that models video-text as game players using multivariate cooperative game theory. We extend our original structure into a flexible encoder-decoder framework, enabling the model to adapt to various downstream tasks.
• Score: 60.44337740854767
• License:
• Abstract: Multimodal representation learning, with contrastive learning, plays an important role in the artificial intelligence domain. As an important subfield, video-language representation learning focuses on learning representations using global semantic interactions between pre-defined video-text pairs. However, to enhance and refine such coarse-grained global interactions, more detailed interactions are necessary for fine-grained multimodal learning. In this study, we introduce a new approach that models video-text as game players using multivariate cooperative game theory to handle uncertainty during fine-grained semantic interactions with diverse granularity, flexible combination, and vague intensity. Specifically, we design the Hierarchical Banzhaf Interaction to simulate the fine-grained correspondence between video clips and textual words from hierarchical perspectives. Furthermore, to mitigate the bias in calculations within Banzhaf Interaction, we propose reconstructing the representation through a fusion of single-modal and cross-modal components. This reconstructed representation ensures fine granularity comparable to that of the single-modal representation, while also preserving the adaptive encoding characteristics of cross-modal representation. Additionally, we extend our original structure into a flexible encoder-decoder framework, enabling the model to adapt to various downstream tasks. Extensive experiments on commonly used text-video retrieval, video-question answering, and video captioning benchmarks, with superior performance, validate the effectiveness and generalization of our method.

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