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.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Transformer Mm Explainability | 490 | a year ago | 5 | mit | Jupyter Notebook | |||||
| [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. | ||||||||||
| Tbd Nets | 335 | 6 years ago | 2 | mit | Jupyter Notebook | |||||
| PyTorch implementation of "Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning" | ||||||||||
| Awesome Vqg | 9 | 4 years ago | ||||||||
| Visual Question Generation reading list | ||||||||||
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