Transformer MM Explainability

What it does

[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.

Best for

Explore AI-assisted workflows, automate parts of your work and create useful first drafts faster.

Key benefits

• Speeds up the first draft or repetitive part of the workflow.
• Reduces manual effort for common unlimited free ai tools tasks.
• Useful for experimentation before final human review.

Common limitations

AI output can be inaccurate or incomplete. Features, privacy terms and usage limits can change, so verify important results on the provider website.

Before you use it

• Check the provider's current pricing and feature limits.
• Do not paste confidential data unless the provider's privacy terms are appropriate.
• Verify important facts, calculations, citations and generated assets.