Official implementation and dataset for the NAACL 2024 paper "ComCLIP: Training-Free Compositional Image and Text Matching"
A deep learning model that generates descriptions of an image.
Image captioning model with Resnet50 encoder and LSTM decoder
Download flickr8k, flickr30k image caption datasets
PyTorch implementation of 'CLIP' (Radford et al., 2021) from scratch and training it on Flickr8k + Flickr30k
Visual Elocution Synthesis
Karpathy Splits json files for image captioning
Processing data produced by flickr30k_entities to use as regional description for densecap model
ImgCap is an image captioning model designed to automatically generate descriptive captions for images. It has two versions CNN + LSTM model and CNN + LSTM + Attention mechanism model.
Attention Based image captioning
Image captioning generation using Swin transformer and GRU attention mechanism
This project aims to provide real-time visual-to-audio conversion, empowering visually impaired users by describing images through generated captions and synthesized audio. The system employs a Transformer-based image captioning model and integrates with a browser extension for seamless functionality.
Implementation of CLIP from OpenAI using pretrained Image and Text Encoders.
"Flickr30k_image_captioning" is a project or repository focused on image captioning using the Flickr30k dataset. The project aims to develop and showcase algorithms and models that generate descriptive captions for images.
Add a description, image, and links to the flickr30k topic page so that developers can more easily learn about it.
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