Visual Question Answering in the Medical Domain VQA-Med 2019
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Updated
Jan 12, 2024
Visual Question Answering in the Medical Domain VQA-Med 2019
CloudCV Visual Question Answering Demo
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
The Easy Visual Question Answering dataset.
Multi-page document understanding and VQA using OCR-free method
Visual Question Generation reading list
VQA-Med 2021
A real-time Visual Question Answering Framework
SciGraphQA: Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs
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.
Counterfactual Reasoning VQA Dataset
[CVPR2021] SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events
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
Grid features extraction for ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering"
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.
B.Sc. Final Project: LXMERT Model Compression for Visual Question Answering.
CLEVR3D Dataset: Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation
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