arya46 / VQA-Flask-AppLinks
A simple Flask app to generate answer given an image and a natural language question about the image. The app uses a deep learning model, trained with Tensorflow, behind the scenes.
☆12Updated 3 years ago
Alternatives and similar repositories for VQA-Flask-App
Users that are interested in VQA-Flask-App are comparing it to the libraries listed below
Sorting:
- BERT + Image Captioning☆135Updated 5 years ago
- This repository gives a GUI using PyQt4 for VQA demo using Keras Deep Learning Library. The VQA model is created using Pre-trained VGG-1…☆46Updated 4 years ago
- Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome☆1,467Updated 2 years ago
- Strong baseline for visual question answering☆240Updated 2 years ago
- ☆23Updated 7 years ago
- Pytorch VQA : Visual Question Answering (https://arxiv.org/pdf/1505.00468.pdf)☆98Updated 2 years ago
- Implemented 3 different architectures to tackle the Image Caption problem, i.e, Merged Encoder-Decoder - Bahdanau Attention - Transformer…☆40Updated 4 years ago
- Vision-Language Pre-training for Image Captioning and Question Answering☆424Updated 3 years ago
- Implemented Image Captioning Model using both Local and Global Attention Techniques and API'fied the model using FLASK☆26Updated 5 years ago
- Image captioning models "show and tell" + "show, attend and tell" in PyTorch☆19Updated 7 years ago
- CNN+LSTM, Attention based, and MUTAN-based models for Visual Question Answering☆77Updated 5 years ago
- BLOCK (AAAI 2019), with a multimodal fusion library for deep learning models☆357Updated 6 years ago
- An efficient PyTorch implementation of the winning entry of the 2017 VQA Challenge.☆764Updated last year
- Visual Question Answering in Pytorch☆734Updated 6 years ago
- generate captions for images using a CNN-RNN model that is trained on the Microsoft Common Objects in COntext (MS COCO) dataset☆81Updated 7 years ago
- PyTorch bottom-up attention with Detectron2☆238Updated 4 years ago
- PyTorch implementation of Image captioning with Bottom-up, Top-down Attention☆168Updated 7 years ago
- Pytorch implementation of VQA: Visual Question Answering (https://arxiv.org/pdf/1505.00468.pdf) using VQA v2.0 dataset for open-ended ta…☆21Updated 5 years ago
- Bilinear attention networks for visual question answering☆547Updated 2 years ago
- Deep Modular Co-Attention Networks for Visual Question Answering☆456Updated 5 years ago
- PyTorch VQA implementation that achieved top performances in the (ECCV18) VizWiz Grand Challenge: Answering Visual Questions from Blind P…☆62Updated 7 years ago
- Automatic image captioning model based on Caffe, using features from bottom-up attention.☆249Updated 2 years ago
- An implementation that downstreams pre-trained V+L models to VQA tasks. Now support: VisualBERT, LXMERT, and UNITER☆165Updated 3 years ago
- Transformer-based image captioning extension for pytorch/fairseq☆318Updated 5 years ago
- Image Captioning using InceptionV3 and beam search☆328Updated 5 years ago
- PyTorch code for EMNLP 2019 paper "LXMERT: Learning Cross-Modality Encoder Representations from Transformers".☆966Updated 3 years ago
- Show and Tell : A Neural Image Caption Generator☆112Updated 5 years ago
- Image Captioning: Implementing the Neural Image Caption Generator☆21Updated 5 years ago
- Code for the paper "VisualBERT: A Simple and Performant Baseline for Vision and Language"☆539Updated 2 years ago
- Generating Captions for images using Deep Learning☆122Updated 7 years ago