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.

Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions. CVPR 2019

A neural network to generate captions for an image using CNN and RNN with BEAM Search.

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

Adds SPICE metric to coco-caption evaluation server codes

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.

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.

A tool for downloading from public image boards (which allow scraping) / preview your images & tags / edit your images & tags. Additional tabs for downloading other desired code repositories as well as S.O.T.A. diffusion and clips models for your purposes. Custom datasets can be added!