Implementation of the paper "Show and Tell: A Neural Image Caption Generator" by Vinyals et al. (CVPR 2015)

wd14 tagger app with UI

Utility to downlaod the Pascal Sentence Dataset

Aid for blinds. This AI will describe the surrounding, it will tell who is in front of him (if that person is a known person to AI using Facial Recognition) and it will also help him to know what is written (Optical Character Recognition)

The BlipProcessor and BlipForConditionalGeneration are likely classes specific to a model called "Blip," which seems to be a transformer-based model for conditional text generation.

Image Captioning is the process of generating a textual description for given images. Image captioning can be regarded as an end-to-end Sequence to Sequence problem, as it converts images, which is regarded as a sequence of pixels to a sequence of words. Image captioning has a huge amount of application.

Streamlit application for Geosciences Image Labeling (Category and caption)

image tagging and captioning through mmdetection and BLIP

oCaption: Leveraging OpenAI's GPT-4 Vision for Advanced Image Captioning

image captioning model pytorch implementation

Public repo for the paper: "COSMic: A Coherence-Aware Generation Metric for Image Descriptions" by Mert İnan, Piyush Sharma, Baber Khalid, Radu Soricut, Matthew Stone, Malihe Alikhani

Basic image recognition application using Gemini Vision Pro and also gets some cool captions for Instagram

The code with an attempt on the disentanglement for the decoder part of a caption generator.

on the construction

[Python 3] Tensorflow implementation of "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention"

Image Caption Generation using Adaptive Attention

Image caption generation using neural network

Smart-I is an android application aimed at helping the visually impaired using artificial intelligence and cloud computing.

interesting python scripts made by myself

Image Caption Generation using Spatial Attention