FATE Model: Achieving Dual Frame-Level Semantic and Temporal Alignment for Audio-Visual
RUC · hf · 2026-08-10
Current audio-visual models often struggle to balance semantic matching with temporal alignment. To bridge this gap, researchers introduced FATE (Frame-level Audio-visual Temporal Embedding).
- Core Methodology: Unlike prior approaches that pool each modality into a single embedding, FATE retains frame-level sequences, aligns them on a physical timeline, and computes similarity over strictly aligned frame pairs. It is trained with a joint objective combining cross-video semantic and within-video temporal contrastive learning.
- Performance: Across three tasks, FATE surpasses the strongest baselines in temporal and semantic retrieval by a large margin. It matches fully supervised methods on event localization in a zero-shot setting and achieves the best correlation with human judgments when used as a generation evaluation metric.
The source code for the project has been open-sourced.
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