Rank #6,457 914 starsJupyter Notebook

Transformer-MM-Explainability

by hila-chefer (personal account)

[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. (description from the project’s GitHub page)

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Stars: 914
Forks: 118
Open issues and pull requests: 14
Primary language: Jupyter Notebook
License: MIT License (MIT)
Owner: hila-chefer · Personal account
Created: Mar 23, 2021
Last push: Aug 24, 2023

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