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DEX-AR: A Dynamic Explainability Method for Autoregressive Vision-Language Models
🤖AI Summary
Researchers developed DEX-AR, a new explainability method for autoregressive Vision-Language Models that generates 2D heatmaps to understand how these AI systems make decisions. The method addresses challenges in interpreting modern VLMs by analyzing token-by-token generation and visual-textual interactions, showing improved performance across multiple benchmarks.
Key Takeaways
- →DEX-AR introduces dynamic head filtering to identify attention heads focused on visual information in autoregressive VLMs.
- →The method generates both per-token and sequence-level 2D heatmaps to explain model decision-making processes.
- →Traditional explainability methods designed for classification tasks struggle with modern autoregressive Vision-Language Models.
- →The approach distinguishes between visually-grounded and purely linguistic tokens during explanation generation.
- →Evaluation on ImageNet, VQAv2, and PascalVOC showed consistent improvements in both perturbation-based and segmentation-based metrics.
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#vision-language-models#explainable-ai#autoregressive#deep-learning#computer-vision#nlp#model-interpretability#attention-mechanisms
Read Original →via arXiv – CS AI
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