Transformer-MM-Explainability

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

visualization

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856 Stars

111 Forks

856 Watchers

Jupyter Notebook Language

mit License

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$131.3K

Market Value

$484.7K

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Growth over time

7 data points · 2021-08-01 → 2025-07-01

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What is the hila-chefer/Transformer-MM-Explainability GitHub project? Description: "[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.". Written in Jupyter Notebook. Explain what it does, its main use cases, key features, and who would benefit from using it.

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