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arXiv · 2508.01711

GAIS: Frame-Level Gated Audio-Visual Integration with Semantic Variance-Scaled Perturbation for Text-Video Retrieval

Bowen Yang·Yun Cao·Chen He·Xiaosu Su
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Abstract

Text-to-video retrieval requires precise alignment between language and temporally rich audio-video signals. However, existing methods often emphasize visual cues while underutilizing audio semantics or relying on coarse fusion strategies, resulting in suboptimal multimodal representations. We introduce GAIS, a retrieval framework that strengthens multimodal alignment from both representation and regularization perspectives. First, a Frame-level Gated Fusion (FGF) module adaptively integrates audio-visual features under textual guidance, enabling fine-grained temporal selection of informative frames. Second, a Semantic Variance-Scaled Perturbation (SVSP) mechanism regularizes the text embedding space by controlling perturbation magnitude in a semantics-aware manner. These two modules are complementary: FGF minimizes modality gaps through selective fusion, while SVSP improves embedding stability and discrimination. Extensive experiments on MSR-VTT, DiDeMo, LSMDC, and VATEX demonstrate that GAIS consistently outperforms strong baselines across multiple retrieval metrics while maintaining notable computational efficiency.

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Bowen Yang, Yun Cao, Chen He, Xiaosu Su. 2025-08-03. GAIS: Frame-Level Gated Audio-Visual Integration with Semantic Variance-Scaled Perturbation for Text-Video Retrieval. https://arxiv.org/abs/2508.01711

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