Harnessing Image Captioning to improve VQA
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Updated
Dec 9, 2017 - Python
Harnessing Image Captioning to improve VQA
The task of Visual Question Answering (VQA) involves generating a natural language answer to a question about an image
Part of our final year project work involving complex NLP tasks along with experimentation on various datasets and different LLMs
Visual Question Answering Using CLIP + LSTM
VQA Challenge - hosted on Hasura using Flask
Reproducibility Challenge - The Neuro-Symbolic Concept Learner
Contains the codes and reports done as part of the AI Club project - AI on Edge
An AI Pathologist for answering all your medical queries.
baselines and neural network models for Visual Question Answering task
A deep learning model with with a web application to answer image-based questions with a non-generative way by carefully curating the answer vocabulary and adding linear layer on top of Open AI's CLIP model as image and text encoder.
This is the coursework of deep learning in UoS
Final project for IA376. Attempting to work with WikiTableQuestions Dataset
Egunean Behin Visual Question Answering Dataset
Deep Learning-Powered Visual & Textual Answering System
[NeurIPS2023] LoRA: A Logical Reasoning Augmented Dataset for Visual Question Answering
Medical Report Generation And VQA (Adapting XrayGPT to Any Modality)
Stacked attention network for open-ended visual Q&A
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