Attention Based image captioning
Image captioning generation using Swin transformer and GRU attention mechanism
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.
Image captioning model with Resnet50 encoder and LSTM decoder
Official implementation and dataset for the NAACL 2024 paper "ComCLIP: Training-Free Compositional Image and Text Matching"
Karpathy Splits json files for image captioning
PyTorch implementation of 'CLIP' (Radford et al., 2021) from scratch and training it on Flickr8k + Flickr30k
Visual Elocution Synthesis
Download flickr8k, flickr30k image caption datasets
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.
Processing data produced by flickr30k_entities to use as regional description for densecap model
A deep learning model that generates descriptions of an image.
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