Feature-level Interaction Explanations in Multimodal Transformers
📰 ArXiv cs.AI
Learn to explain multimodal transformer predictions by analyzing feature-level interactions between different modalities
Action Steps
- Apply multimodal explainable AI (MXAI) methods to extend unimodal saliency to multimodal backbones
- Identify important tokens or patches within each modality
- Analyze cross-modal feature pairs to detect synergy and redundancy
- Use feature-level interaction explanations to clarify how different modalities support a decision
- Evaluate the effectiveness of feature-level interaction explanations in improving model interpretability
Who Needs to Know This
Data scientists and AI engineers working with multimodal transformers can benefit from this technique to improve model interpretability and transparency
Key Insight
💡 Feature-level interaction explanations can reveal how different modalities jointly support a decision in multimodal transformers
Share This
🤖 Improve multimodal transformer interpretability with feature-level interaction explanations! #MXAI #MultimodalTransformers
Full Article
Title: Feature-level Interaction Explanations in Multimodal Transformers
Abstract:
arXiv:2603.13326v2 Announce Type: replace-cross Abstract: Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision. Most existing multimodal explainable AI (MXAI) methods extend unimodal saliency to multimodal backbones, highlighting important tokens or patches within each modality, but they rarely pinpoint which cross-modal feature pairs provide complementary evidence (synergy) or serve as reliable backups (redundancy). We present
Abstract:
arXiv:2603.13326v2 Announce Type: replace-cross Abstract: Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision. Most existing multimodal explainable AI (MXAI) methods extend unimodal saliency to multimodal backbones, highlighting important tokens or patches within each modality, but they rarely pinpoint which cross-modal feature pairs provide complementary evidence (synergy) or serve as reliable backups (redundancy). We present
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