vqa-dataset
There are 38 repositories under vqa-dataset topic.
vztu/BVQA_Benchmark
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
abachaa/VQA-Med-2019
Visual Question Answering in the Medical Domain VQA-Med 2019
Cloud-CV/VQA
CloudCV Visual Question Answering Demo
sutdcv/SUTD-TrafficQA
[CVPR2021] SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events
findalexli/SciGraphQA
SciGraphQA: Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs
vzhou842/easy-VQA
The Easy Visual Question Answering dataset.
CAMMA-public/SSG-VQA
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.
badripatro/awesome-vqg
Visual Question Generation reading list
Letian2003/C-VQA
Counterfactual Reasoning VQA Dataset
abachaa/VQA-Med-2021
VQA-Med 2021
yanx27/CLEVR3D
CLEVR3D Dataset: Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation
google-research-datasets/maverics
MAVERICS (Manually-vAlidated Vq^2a Examples fRom Image-Caption datasetS) is a suite of test-only benchmarks for visual question answering (VQA).
yousefkotp/Visual-Question-Answering
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
csebuetnlp/IllusionVQA
This repository contains the data and code of the paper titled "IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models"
chakravarthi589/Video-Question-Answering_Resources
Video Question Answering | Video QA | VQA
lisamalani/VLR_term_project
Multi-page document understanding and VQA using OCR-free method
fraction-ai/GAP
Gamified Adversarial Prompting (GAP): Crowdsourcing AI-weakness-targeting data through gamification. Boost model performance with community-driven, strategic data collection
ghazaleh-mahmoodi/lxmert_compression
B.Sc. Final Project: LXMERT Model Compression for Visual Question Answering.
VibhuJawa/vqa-2018
This repo implements attention networks for visual question answering
gutbash/lmm-graph-vision
How well do the GPT-4V, Gemini Pro Vision, and Claude 3 Opus models perform zero-shot vision tasks on data structures?
manoja328/vqatools
API for VQA , visual 7w dataset
zeryabmoussaoui/Real-time-VQA
A real-time Visual Question Answering Framework
IAmS4n/Visual-Question-Answering
Investigation on VQA dataset. TensorFlow is utilized for the implementation of a solution based on CNN and RNN architectures plus some ideas such as Attention and Positional features.
juletx/egunean-behin-vqa
Egunean Behin Visual Question Answering Dataset
radonys/CFB-VQA
VQA Challenge - hosted on Hasura using Flask
rentainhe/TRAR-Feature-Extraction
Grid features extraction for ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering"
abdur75648/MedicalGPT
Medical Report Generation And VQA (Adapting XrayGPT to Any Modality)
shivam1423/VQA
Visual Question Answer (VQA) software! Powered by Flask, this project seamlessly combines images and questions to generate accurate responses. Explore the world of interactive visual understanding with ease.
thatAverageGuy/EarlyFusion-on-EasyVQA
Streamlit app for demonstrating multi-modal(vision+language) modelling in Pytorch.
dinesh-kumar-mr/MediVQA
Part of our final year project work involving complex NLP tasks along with experimentation on various datasets and different LLMs
MuhammadShavaiz/DL-Visual-Question-Answering
The Visual Question Answering (VQA) project features a model with a simple GUI that handles both images and videos. It uses OpenAI's CLIP for encoding images and questions and GPT-2 for decoding embeddings to answer questions based on the VQA Version 2 dataset, which includes 265,016 images with multiple questions and answers.