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Grad-ECLIP: Gradient-based Visual and Textual Explanations for CLIP
Summary
We introduce Grad-ECLIP, a new method for explaining Contrastive Language-Image Pre-training (CLIP) model results. Grad-ECLIP generates heat maps to show how image regions and words influence CLIP
Area of Science:
- Artificial Intelligence
- Computer Vision
- Natural Language Processing
Background:
- Contrastive Language-Image Pre-training (CLIP) models have advanced significantly in performance and applications.
- However, the interpretability of CLIP's decision-making process remains underexplored.
- Understanding CLIP's inner workings is crucial for trust and further development.
Purpose of the Study:
- To develop a novel interpretation method for CLIP's image-text matching.
- To provide visual and textual explanations for CLIP's predictions.
- To analyze CLIP's behavior and limitations through its interpretation.
Main Methods:
- Propose Gradient-based visual and textual Explanation method for CLIP (Grad-ECLIP).
- Decompose CLIP's encoder architecture to link matching similarity with spatial features.
- Generate heat maps by applying channel and spatial weights to token features, bypassing sparse self-attention maps.
Main Results:
- Grad-ECLIP produces high-quality visual explanations, outperforming existing methods.
- Evaluations confirm the effectiveness and superiority of Grad-ECLIP.
- Analysis reveals insights into image-text matching mechanisms and CLIP's attribution identification.
Conclusions:
- Grad-ECLIP offers a robust solution for interpreting CLIP models.
- The method enhances understanding of CLIP's strengths, limitations, and word usage patterns.
- This work paves the way for more transparent and reliable vision-language models.
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