Stars
Official repository of "SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory"
Kindling the Darkness: a Practical Low-light Image Enhancer
A curated list of awesome TikZ documentations, libraries and resources
Drawing Bayesian networks, graphical models, tensors, technical frameworks, and illustrations in LaTeX.
Official implementation of SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference
Tips for Writing a Research Paper using LaTeX
[ECCV 2024] Official implementation of the paper "X-Pose: Detecting Any Keypoints"
Mirage: a zero-shot cross-embodiment policy transfer method. Benchmarking code for cross-embodiment policy transfer.
This repo hosts the code for the Fast Trainable Projection (FTP) project.
This repo contains the evaluation code for the INQUIRE benchmark
🚀 Level up your GitHub profile readme with customizable cards including LOC statistics!
A curated publication list on open vocabulary semantic segmentation and related area (e.g. zero-shot semantic segmentation) resources..
Refine high-quality datasets and visual AI models
Open-Sora: Democratizing Efficient Video Production for All
Accepted as [NeurIPS 2024] Spotlight Presentation Paper
Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous …
[ICCV2023] VLPart: Going Denser with Open-Vocabulary Part Segmentation
[NeurIPS'23] Emergent Correspondence from Image Diffusion
The official homepage of the COCO-Stuff dataset.
The image prompt adapter is designed to enable a pretrained text-to-image diffusion model to generate images with image prompt.
InstantID: Zero-shot Identity-Preserving Generation in Seconds 🔥
This project aim to reproduce Sora (Open AI T2V model), we wish the open source community contribute to this project.
【CVPR 2024 Highlight】Monkey (LMM): Image Resolution and Text Label Are Important Things for Large Multi-modal Models
Official code for "FeatUp: A Model-Agnostic Frameworkfor Features at Any Resolution" ICLR 2024