Repository History
19 repositories tagged with Computer Vision
AWESOME-OCR-LLM: Curated Reading List for OCR in the LLM Era
AWESOME-OCR-LLM is a continuously updated reading list focusing on Optical Character Recognition (OCR) in the era of large language models (LLMs). It covers key areas like document parsing, understanding, visual text generation, and benchmarks, highlighting research from the past five years. This resource is invaluable for anyone tracking the rapid advancements in multimodal document AI.
scikit-video: Video Processing Routines for SciPy
scikit-video is a Python library designed for video processing, offering a suite of routines for tasks like I/O, quality metrics, and temporal filtering. Intended as a companion to scikit-image, it provides video-specific algorithms and aims for flexibility and GPU compute capabilities. This project offers a research-oriented alternative to existing frameworks, built entirely in Python.
Ludwig: Low-Code Declarative Deep Learning for LLMs and AI Models
Ludwig is a powerful, low-code declarative deep learning framework designed for building custom LLMs, neural networks, and other AI models. It simplifies the process of training, fine-tuning, and deploying models, from LLM fine-tuning to tabular classification, using a simple YAML configuration without boilerplate Python code. This makes advanced AI development accessible and efficient for a wide range of applications.
PyTorch Image Models (timm): The Ultimate Collection of Image Encoders
PyTorch Image Models (timm) is an extensive library offering the largest collection of PyTorch image encoders and backbones. It provides a wide array of state-of-the-art models, complete with pretrained weights, training, evaluation, and inference scripts. This makes it an invaluable resource for researchers and developers working with computer vision tasks in PyTorch.
CoTracker: A Powerful Model for Tracking Any Point on a Video
CoTracker is a state-of-the-art model developed by Facebook AI Research and the University of Oxford, designed for tracking any point (pixel) across video sequences. This transformer-based solution offers fast, accurate, and quasi-dense point tracking capabilities. It is an invaluable tool for researchers and developers in computer vision, enabling precise analysis of motion in videos.
WiFi-3D-Fusion: Real-Time 3D Human Pose Estimation from WiFi Signals
WiFi-3D-Fusion is an innovative open-source research project that leverages WiFi CSI signals and deep learning to estimate 3D human pose. It uniquely fuses wireless sensing with computer vision techniques, providing next-generation spatial awareness. This project offers real-time motion detection and visualization, showcasing a novel approach to understanding human movement in 3D space.
GigaSLAM: Large-Scale Monocular SLAM with Hierarchical Gaussian Splats
GigaSLAM is a groundbreaking monocular SLAM framework designed for kilometer-scale outdoor environments. It leverages hierarchical Gaussian splats and neural networks to achieve efficient, scalable mapping and high-fidelity rendering. This system addresses the challenges of large-scale tracking and mapping using only RGB input, extending the applicability of Gaussian Splatting SLAM to unbounded outdoor scenes.
MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation
MonoPCC is a PyTorch implementation for monocular depth estimation, specifically designed for endoscopic images using a photometric-invariant cycle constraint. This self-supervised learning approach aims to improve depth prediction accuracy in challenging medical imaging scenarios. It demonstrates state-of-the-art performance on datasets like SCARED and KITTI, and offers a plug-and-play design for integration into various backbone networks.
big_vision: Google Research's Codebase for Large-Scale Vision Models
big_vision is Google Research's official codebase for training large-scale vision models using Jax/Flax. It has been instrumental in developing prominent architectures like Vision Transformer, SigLIP, and MLP-Mixer. This repository offers a robust starting point for researchers to conduct scalable vision experiments on GPUs and Cloud TPUs, scaling seamlessly from single cores to distributed setups.
HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation
HunyuanVideo-Avatar is a cutting-edge project by Tencent-Hunyuan for high-fidelity, audio-driven human animation. Utilizing a multimodal diffusion transformer, it generates dynamic, emotion-controllable, and multi-character dialogue videos. This innovative system addresses critical challenges in character consistency, emotion alignment, and multi-character animation, making it suitable for diverse applications like e-commerce and social media.
OmniParser: A Vision-Based Tool for GUI Agent Screen Parsing
OmniParser is a comprehensive tool developed by Microsoft for parsing user interface screenshots into structured, understandable elements. It significantly enhances the ability of vision-based models, such as GPT-4V, to generate accurate actions grounded in specific regions of a GUI. This project aims to advance pure vision-based GUI agents by providing robust screen parsing capabilities.
CineScale: Unlocking 4K High-Resolution Cinematic Video Generation
CineScale is an innovative GitHub repository by Eyeline-Labs, extending FreeScale to enable high-resolution cinematic video generation. It provides models and tools to achieve up to 4K video output, leveraging diffusion models for advanced visual content creation. This project offers a robust framework for researchers and developers to generate stunning, high-definition videos.