CloudCV Visual Question Answering Demo
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Updated
Nov 4, 2022 - Lua
CloudCV Visual Question Answering Demo
Visual Question Answering in the Medical Domain VQA-Med 2019
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
A real-time Visual Question Answering Framework
SciGraphQA
Counterfactual Reasoning VQA Dataset
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.
[CVPR2021] SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events
VQA-Med 2021
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.
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
CLEVR3D Dataset: Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation
B.Sc. Final Project: LXMERT Model Compression for Visual Question Answering.
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