How are relevance scores from visual, textual, and audio modalities combined?

Combining relevance scores from visual, textual, and audio modalities typically involves a multi-step process that aligns and weights features from each modality to produce a unified representation. Here’s a structured explanation tailored for developers:

1. Alignment and Feature Fusion

Modalities like text, audio, and visual data are first encoded into numerical representations using pre-trained models (e.g., BERT for text, CNNs for images). These features are then aligned to a common dimensional space. For example, a 1D convolutional network might standardize the dimensions of visual and audio features to match textual embeddings [1]. Cross-modal attention mechanisms, such as Self-Attention or Cross-Attention, are often used to identify relationships between modalities. For instance, Ref-AVS integrates audio and text cues by computing cross-attention scores between audio signals and visual regions, enabling the model to focus on relevant objects in dynamic scenes [2].

2. Weighted Combination and Hierarchical Processing

After alignment, modalities are combined using weighted fusion. This involves dynamically adjusting the contribution of each modality based on task-specific relevance. In Ref-AVS, audio and text modalities are assigned distinct attention tokens, and their interactions are modeled through hierarchical fusion layers [2]. Similarly, methods like Recursive Joint Cross-Modal Attention (RJCMA) recursively refine relevance scores by capturing intra- and inter-modal dependencies—for example, correlating audio pitch changes with facial expressions in emotion recognition [10]. Residual connections and normalization (e.g., layer normalization) are added to stabilize training [1][7].

3. Post-Fusion Optimization

The fused representation is further processed for downstream tasks like classification or segmentation. For example, in emotion analysis, fused features are multiplied with text-based attention matrices and passed through a classifier to fine-tune the model [1][7]. Challenges include handling modality-specific noise (e.g., irrelevant visual objects in videos) and computational efficiency. Techniques like global audio feature enhancement address this by prioritizing temporally consistent audio patterns over transient visual noise [7].

Key Considerations for Developers

• Modality imbalance: Text often dominates in cross-modal tasks, so techniques like masked fusion (suppressing less relevant modalities) are useful [1].
• Temporal alignment: Audio-visual tasks require synchronizing features across time steps (e.g., aligning speech with lip movements) [10].
• Scalability: Pre-extracting modality-specific features (e.g., using VGG for visuals) reduces runtime complexity during fusion [10].