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https://github.com/hila-chefer/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.
https://github.com/hila-chefer/Transformer-MM-Explainability
clip detr explainability explainable-ai interpretability lxmert transformer transformers visualbert visualization vqa
Last synced: 3 months ago
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[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.
- Host: GitHub
- URL: https://github.com/hila-chefer/Transformer-MM-Explainability
- Owner: hila-chefer
- License: mit
- Created: 2021-03-23T22:11:18.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-08-24T17:45:14.000Z (over 1 year ago)
- Last Synced: 2024-08-01T13:24:10.066Z (6 months ago)
- Topics: clip, detr, explainability, explainable-ai, interpretability, lxmert, transformer, transformers, visualbert, visualization, vqa
- Language: Jupyter Notebook
- Homepage:
- Size: 25.3 MB
- Stars: 758
- Watchers: 8
- Forks: 104
- Open Issues: 11
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Metadata Files:
- Readme: README.rst
- License: LICENSE