BierOne/genome-rcnn-features-for-bottom-up

Stars
1
Language
Created almost 5 years ago
Updated over 3 years ago

BierOne/genome-rcnn-features-for-bottom-up

BierOne

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

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

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'

ood_coverage

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

Attention-Faithfulness

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

relation-vqa

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

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).

policy_privacy_benchmarks

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