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attention-faithfulness

[ICML 2022] This is the pytorch implementation of "Rethinking Attention-Model Explainability through Faithfulness Violation Test" (https://arxiv.org/abs/2201.12114).

attentionexplanation

bottom-up-attention-vqa

An updated PyTorch implementation of hengyuan-hu's version for 'Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering'

coco

diffusers-interpret

Diffusers-Interpret 🤗🧨🕵️‍♀️: Model explainability for 🤗 Diffusers. Get explanations for your generated images.

directclr

easyedit

An Easy-to-use Knowledge Editing Framework for LLMs.

genome-rcnn-features-for-bottom-up

This repository supplies the visual-genome features of bottom-up-attention

interpretable-attention

Official Code for Towards Transparent and Explainable Attention Models paper (ACL 2020)

knowledgeediting_benchmarks

This repository serves as a comprehensive resource for researchers interested in exploring Knowledge Editing. Here, you'll find detailed comparisons and information about various benchmarks/datasets relevant to this domain.

negative_analysis_of_grounding

A negative case analysis of visual grounding methods for VQA (ACL 2020 short paper)

netdissect-gen

ood_coverage

[ICLR 2024 Spotlight] Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization

policy_privacy_benchmarks

This is a pytorch implementation for state-of-the-art results on policy privacy datasets (OPP-115).

relation-vqa

Re-implementation for 'R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering'.

stable-diffusion-webui

Stable Diffusion web UI

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.

unlearndiffusion

vqa-attreg

This is an official PyTorch implementation of “Answer Questions with Right Image Regions: A Visual Attention Regularization Approach” (https://arxiv.org/abs/2102.01916).