Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome
awesome grounding: A curated list of research papers in visual grounding
Meshed-Memory Transformer for Image Captioning. CVPR 2020
Learning to ground explanations of affect for visual art.
A neural network to generate captions for an image using CNN and RNN with BEAM Search.
Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions. CVPR 2019
A modular library built on top of Keras and TensorFlow to generate a caption in natural language for any input image.
Automatic image captioning model based on Caffe, using features from bottom-up attention.
📸 Generates hashtags for Instagram posts. Upload your photo and it will suggest the relevant #hashtags for you. 🏷️ 
This repository contains my solutions to the assignments for Stanford's CS231n "Convolutional Neural Networks for Visual Recognition" (Spring 2020).
[DEPRECATED] A Neural Network based generative model for captioning images using Tensorflow
Semantic Propositional Image Caption Evaluation
VisText is a benchmark dataset for semantically rich chart captioning.
Implementation of Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space
Positive-Augmented Contrastive Learning for Image and Video Captioning Evaluation. CVPR 2023
EMNLP 2018. Learning to Describe Differences Between Pairs of Similar Images. Harsh Jhamtani, Taylor Berg-Kirkpatrick.
Give your computer an AI Brain
Adds SPICE metric to coco-caption evaluation server codes
PyTorch code for: Learning to Generate Grounded Visual Captions without Localization Supervision
Display an image and text file side-by-side for easy manual caption editing.
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