A resource list and performance benchmark for blind video quality assessment (BVQA) models on user-generated content (UGC) datasets. [IEEE TIP'2021] "UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content", Zhengzhong Tu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik

Visual Question Answering in the Medical Domain VQA-Med 2019

CloudCV Visual Question Answering Demo

[CVPR2021] SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events

SciGraphQA: Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs

The Easy Visual Question Answering dataset.

SSG-VQA is a Visual Question Answering (VQA) dataset on laparoscopic videos providing diverse, geometrically grounded, unbiased and surgical action-oriented queries generated using scene graphs.

Visual Question Generation reading list

Counterfactual Reasoning VQA Dataset

VQA-Med 2021

CLEVR3D Dataset: Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation

MAVERICS (Manually-vAlidated Vq^2a Examples fRom Image-Caption datasetS) is a suite of test-only benchmarks for visual question answering (VQA).

A Light weight deep learning model with with a web application to answer image-based questions with a non-generative approach for the VizWiz grand challenge 2023 by carefully curating the answer vocabulary and adding linear layer on top of Open AI's CLIP model as image and text encoder

This repository contains the data and code of the paper titled "IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models"

Video Question Answering | Video QA | VQA

Multi-page document understanding and VQA using OCR-free method

B.Sc. Final Project: LXMERT Model Compression for Visual Question Answering.

Gamified Adversarial Prompting (GAP): Crowdsourcing AI-weakness-targeting data through gamification. Boost model performance with community-driven, strategic data collection

This repo implements attention networks for visual question answering