SD.Next: All-in-one WebUI for AI generative image and video creation, captioning and processing

Efficient Image Captioning code in Torch, runs on GPU

Keras model to generate HTML code from hand-drawn website mockups. Implements an image captioning architecture to drawn source images.

Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning

The NCA Toolkit API eliminates monthly subscription fees by consolidating common API functionalities into a single FREE API. Designed for businesses, creators, and developers, it streamlines advanced media processing, including video editing and captioning, image transformations, cloud storage, and Python code execution.

Caption-Anything is a versatile tool combining image segmentation, visual captioning, and ChatGPT, generating tailored captions with diverse controls for user preferences. https://huggingface.co/spaces/TencentARC/Caption-Anything https://huggingface.co/spaces/VIPLab/Caption-Anything

提示词小助手可以一键调用智谱、硅基流动、gemini、本地ollama、百度等大语言模型服务，实现提示词翻译、润色扩写、图片反推。支持提示词预设实现一键插入、历史提示词查找等功能。是一个全能型提示词插件。The Prompt Assistant enables one-click access to LLMs/VLMs for prompt translation, expansion, and image captioning. It also supports one-click preset insertion and historical prompt search.

Dense image captioning in Torch

I decide to sync up this repo and self-critical.pytorch. (The old master is in old master branch for archive)

[ICCV 2025] Implementation for Describe Anything: Detailed Localized Image and Video Captioning

Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

Simple image captioning model

JoyCaption is an image captioning Visual Language Model (VLM) being built from the ground up as a free, open, and uncensored model for the community to use in training Diffusion models.

A curated list of image captioning and related area resources. :-)

Unofficial pytorch implementation for Self-critical Sequence Training for Image Captioning. and others.

X-modaler is a versatile and high-performance codebase for cross-modal analytics(e.g., image captioning, video captioning, vision-language pre-training, visual question answering, visual commonsense reasoning, and cross-modal retrieval).

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

Conceptual Captions is a dataset containing (image-URL, caption) pairs designed for the training and evaluation of machine learned image captioning systems.

Meshed-Memory Transformer for Image Captioning. CVPR 2020

Dank Learning codebase, generate a meme from any image using AI. Uses a modified version of the Show and Tell image captioning network

Vision-Language Pre-training for Image Captioning and Question Answering

This repository focus on Image Captioning & Video Captioning & Seq-to-Seq Learning & NLP

Code for paper "Attention on Attention for Image Captioning". ICCV 2019

Implementation of "Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning"

Image Captioning using InceptionV3 and beam search

Transformer-based image captioning extension for pytorch/fairseq

Implementation of 'X-Linear Attention Networks for Image Captioning' [CVPR 2020]

Image Captioning Using Transformer

Hybrid RAG system combining vector search, knowledge graph (LightRAG), and cross-encoder reranking — with Docling document parsing, visual intelligence (image/table captioning), agentic streaming chat, and inline citations. Powered by Gemini or local Ollama models.

Code for "Aligning Linguistic Words and Visual Semantic Units for Image Captioning", ACM MM 2019