Image Captioning with Keras

CaptionBot : Sequence to Sequence Modelling where Encoder is CNN(Resnet-50) and Decoder is LSTMCell with soft attention mechanism

Official implementation and dataset for the NAACL 2024 paper "ComCLIP: Training-Free Compositional Image and Text Matching"

An Image captioning web application combines the power of React.js for front-end, Flask and Node.js for back-end, utilizing the MERN stack. Users can upload images and instantly receive automatic captions. Authenticated users have access to extra features like translating captions and text-to-speech functionality.

Implementation of 'merge' architecture for generating image captions from paper "What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?" using Keras. Dataset used is Flickr8k available on Kaggle.

An attention based sequential deep learning model implemented in pytorch to generate single line caption given an input image

Yet another im2txt (show and tell: A Neural Image Caption Generator)

Karpathy Splits json files for image captioning

Fabricating a Python application that generates a caption for a selected image. Involves the use of Deep Learning and NLP Frameworks in Tensorflow, Keras and NLTK modules for data processing and creation of deep learning models and their evaluation.

Generate captions from images

Image Captioning With MobileNet-LLaMA 3

In this project, we use a Deep Recurrent Architecture, which uses CNN (VGG-16 Net) pretrained on ImageNet to extract 4096-Dimensional image feature Vector and an LSTM which generates a caption from these feature vectors.

Comparitive analysis of image captioning model using RNN, BiLSTM and Transformer model architectures on the Flickr8K dataset and InceptionV3 for image feature extraction.

Generating Captions for images using CNN & LSTM on Flickr8K dataset.The generation of captions from images has various practical benefits, ranging from aiding the visually impaired.

Image Captioning using Deep learning models in Keras.

Implementation of Image Captioning Model using CNNs and LSTMs

🚀 Image Caption Generator Project 🚀 🧠 Building Customized LSTM Neural Network Encoder model with Dropout, Dense, RepeatVector, and Bidirectional LSTM layers. Sequence feature layers with Embedding, Dropout, and Bidirectional LSTM layers. Attention mechanism using Dot product, Softmax attention scores,...

Automatically generating the captions for an image.

Image Captioning is the task of describing the content of an image in words. This task lies at the intersection of computer vision and natural language processing.

The concept of the project is to generate Arabic captions from the Arabic Flickr8K dataset, the tools that were used are the pre-trained CNN (MobileNet-V2) and the LSTM model, in addition to a set of steps using the NLP. The aim of the project is to create a solid ground and very initial steps in order to help children with learning difficulties.