clip

41 proyectos comparten este topic de GitHub

Las líneas conectan a los miembros que están mediblemente relacionados entre sí. El tamaño de los puntos refleja las estrellas.

🧬 Miembros

Open-source AI-assisted annotation platform for images, videos, text, and multimodal data.

Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.

Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks

OpenMMLab Pre-training Toolbox and Benchmark

中文nlp解决方案(大模型、数据、模型、训练、推理)

Collection of AWESOME vision-language models for vision tasks

Easily compute clip embeddings and build a clip retrieval system with them

hcaptcha-challenger

🥂 Gracefully face hCaptcha challenge with multimodal large language model.

Cambrian-1 is a family of multimodal LLMs with a vision-centric design.

awesome-openai-vision-api-experiments

Must-have resource for anyone who wants to experiment with and build on the OpenAI vision API 🔥

[ACL 2024 🔥] Video-ChatGPT is a video conversation model capable of generating meaningful conversation…

awesome-vlm-architectures

Famous Vision Language Models and Their Architectures

Pocket-Sized Multimodal AI for content understanding and generation across multilingual texts, images, and…

vlms-zero-to-hero

This series will take you on a journey from the fundamentals of NLP and Computer Vision to the cutting edge…

Stable-Diffusion-NCNN

Stable Diffusion in NCNN with c++, supported txt2img and img2img

natural-language-image-search

Search photos on Unsplash using natural language

An official implementation for "CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval"

natural-language-youtube-search

Search inside YouTube videos using natural language

Transformer-MM-Explainability

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting…

CLIP + FFT/DWT/RGB = text to image/video

Paddle Multimodal Integration and eXploration, supporting mainstream multi-modal tasks, including end-to-end…

Vision-Language-Models-Overview

A most Frontend Collection and survey of vision-language model papers, and models GitHub repository.…

SkyPaint-AI-Diffusion

awesome-foundation-and-multimodal-models

👁️ + 💬 + 🎧 = 🤖 Curated list of top foundation and multimodal models! [Paper + Code + Examples…

keras_cv_attention_models

Keras beit,caformer,CMT,CoAtNet,convnext,davit,dino,efficientdet,edgenext,efficientformer,efficientnet,eva,fas…

Offline semantic Text-to-Image and Image-to-Image search on Android powered by quantized state-of-the-art…

CLIP inference in plain C/C++ with no extra dependencies

CLIPort: What and Where Pathways for Robotic Manipulation

🔍 Search local images with natural language on Android, powered by OpenAI's CLIP model. / 在 Android…

Transformers-for-NLP-and-Computer-Vision-3rd-Edition

Transformers 3rd Edition

diffusion-explainer

Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion

OCR自动化阅卷项目

[Pattern Recognition 25] CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks

EVE Series: Encoder-Free Vision-Language Models from BAAI

Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model

Free tool to create viral videos from YouTube, generating clips optimized for TikTok and Instagram with…

Generating Robotic Simulation Tasks via Large Language Models

[CVPR2024] ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts

[ECCV 2024] Improving 2D Feature Representations by 3D-Aware Fine-Tuning

Disco_Diffusion_Local

Getting the latest versions of Disco Diffusion to work locally, instead of colab. Including how I run this on…

World's fastest and most compact embedded vector database: exact by default, multimodal, local-first, and…

🔗 Familias relacionadas

llava 26 segment-anything 25 image-search 19 vision-language-model 60 vision-language 18 bert 82 semantic-search 101 image-text-retrieval 5

Medido a partir de los temas de GitHub compartidos por ambos proyectos, ponderado por cuán raros son cada uno de los temas.