Research & Education

FastGS is an acceleration framework designed to supercharge 3D Gaussian Splatting training, enabling state-of-the-art results within 100 seconds. This represents a substantial speed improvement, being 3.32 times faster than DashGaussian on the Mip-NeRF 360 dataset and offering a 15.45 times acceleration compared to vanilla 3DGS on Deep Blending. Despite its rapid training, FastGS maintains comparable rendering quality to other state-of-the-art methods. The framework is highly versatile, seamlessly integrating with various backbones like Vanilla 3DGS, Scaffold-GS, and Mip-splatting. It is also proven effective across multiple tasks, including dynamic scenes, surface reconstruction, sparse-view, large-scale, and SLAM tasks. FastGS is memory-efficient, requiring low GPU memory, and offers easy deployment with a simple post-training tool.

GigaBrain is an AI-powered search platform designed to tap into the collective wisdom of online communities such as Reddit and YouTube. It works by scanning and analyzing billions of comments and discussions across these platforms to find authentic answers to user queries. The tool then compiles these relevant answers into a concise format, aiming to save users significant time and effort in gathering information. A 'PRO' version, GigaBrain PRO, is mentioned to offer access to more powerful AI models, suggesting enhanced capabilities or broader search scope.

LuaRadio is a lightweight and embeddable flow graph signal processing framework specifically designed for software-defined radio (SDR). Built on LuaJIT, it offers a small binary footprint and no external hard dependencies, making it highly portable. The framework provides a comprehensive suite of source, sink, and processing blocks, along with a simple API for defining and running flow graphs, creating custom blocks, and managing data types. It's ideal for rapidly prototyping software radios, developing modulation/demodulation utilities, and conducting signal processing experiments. LuaRadio can also be embedded into existing radio applications, serving as a user-scriptable engine for advanced signal processing tasks. It supports computational acceleration through LuaJIT's FFI to wrap external libraries like VOLK, liquid-dsp, and others, ensuring efficient performance.

llm-twin-course is a free educational resource designed to guide users through the process of building a production-ready Large Language Model (LLM) and Retrieval Augmented Generation (RAG) system. The course emphasizes LLMOps best practices, offering practical, hands-on lessons and accompanying source code. It covers the entire development lifecycle, from initial data gathering to the final stages of productionizing LLMs, with a specific focus on creating an AI replica.

MineContext is an open-source, proactive context-aware AI partner designed to enhance productivity by understanding your digital environment. It captures screenshots and comprehends content, with future support for multi-source multimodal information like documents, images, and videos. Based on a contextual engineering framework, it actively delivers high-quality information such as insights, daily/weekly summaries, to-do lists, and activity records. Key features include effortless context collection, intelligent resurfacing of relevant information during creation, and proactive delivery of summarized content. MineContext prioritizes privacy with local-first data storage and support for local AI models compatible with the OpenAI API protocol, ensuring data remains on your device.

MY-BASIC is a lightweight BASIC interpreter implemented in standard C, provided in dual files for easy integration. It aims to be highly embeddable, extendable, and portable across various platforms. The interpreter supports dynamic typing, structured syntax, and a unique blend of prototype-based object-oriented programming with functional programming via lambda abstraction. Its core is compact, allowing it to be used as a standalone interpreter or seamlessly embedded into existing projects developed in C, C++, Java, Objective-C, Swift, C#, and more. Developers can customize its functionality by adding their own scripting interfaces, making it a versatile tool for various programming needs.

ObjectDetection-OneStageDet is an open-source object detection framework developed by Tencent, designed to provide a unified platform for single-stage generic object detectors. Currently, it supports YOLOv2 and YOLOv3 implementations, with future plans to integrate YOLO and SSD into a single framework. The tool emphasizes performance and speed, offering good mAP scores and fast inference times, especially with various efficient backbones like TinyYOLO, MobileNet, and ShuffleNet. It provides comprehensive instructions for installation, data preparation, training, evaluation, and benchmarking, making it suitable for developers and researchers working on object detection tasks.

Realtime_Multi-Person_Pose_Estimation is an open-source code repository designed for real-time multi-person pose estimation. This tool utilizes a bottom-up approach, which means it does not require a person detector, simplifying the process and improving efficiency. It gained significant recognition by winning the 2016 MSCOCO Keypoints Challenge and the 2016 ECCV Best Demo Award. Further solidifying its academic standing, the project was featured in a 2017 CVPR Oral paper. This makes it a valuable resource for researchers and developers working on computer vision tasks involving human pose analysis.

Rofunc is an open-source Python package designed for robot learning from demonstration and robot manipulation. It provides a comprehensive framework for developing and deploying advanced robot learning algorithms. The tool is hosted on GitHub, making it accessible for researchers and developers in the robotics field. Rofunc facilitates the entire workflow, from initial algorithm development to practical deployment, supporting various aspects of robot control and interaction. Its open-source nature encourages community contributions and collaborative development, making it a valuable resource for advancing robotics research and applications.

Stereo-RCNN is an open-source implementation for accurate 3D object detection and estimation, primarily developed for autonomous driving applications. This tool leverages stereo images to perform simultaneous object detection and association, enhancing the precision of 3D box estimations. It also incorporates a dense alignment module for refining 3D box predictions. The project supports Pytorch 1.0.0 and Python 3.6, with a light-weight version available for scenarios with limited GPU memory. Researchers and developers can utilize Stereo-RCNN for tasks requiring robust 3D perception from image-only data, offering a valuable resource for advancing autonomous systems.

vedadet is a single-stage object detection toolbox built on PyTorch, offering a modular design that re-engineers MMDetection for enhanced flexibility and deployment. It decomposes the detector into four key parts: data pipeline, model, postprocessing, and criterion, making it straightforward to convert PyTorch models into TensorRT engines. This design facilitates efficient deployment on NVIDIA devices such as Tesla V100, Jetson Nano, and Jetson AGX Xavier. The toolbox supports several popular single-stage detectors, including RetinaNet and FCOS, right out of the box. Its friendly integration with TensorRT allows for easy model conversion and deployment through both Python and C++ front-ends, making it a powerful tool for developers working on object detection tasks.

TempestV0.1 GPU Demo is a demonstration of AI capabilities, specifically designed to showcase the TempestV0.1 model. Hosted on Hugging Face Spaces, this tool leverages GPU processing to provide a platform for users to explore and test the model's functionalities. While currently paused, it aims to offer insights into advanced AI applications. Users interested in utilizing this Space are encouraged to contact the author through the community tab to request its restart, indicating its potential for academic research and educational purposes.

InfoShelves Workday Certification app is an AI-driven platform designed to help professionals prepare for Workday certifications. It offers authentic certification practice tests and study notes across various Workday areas, including HCM Pro, Financials, Reporting, and Integration. The platform aims to provide a focused and efficient way to study and pass Workday certification exams, boosting career prospects. With AI-driven discovery, users can master Workday concepts and prepare effectively for their professional development. The app focuses on providing comprehensive exam simulators to ensure users are well-prepared for their certification journey.

Write Breeze is an AI-powered writing assistant designed to help users improve their writing. It provides a suite of tools including paraphrasing to rephrase text, summarizing to condense information, grammar correction to refine language, and translation services. The platform supports a diverse range of languages, such as English, Turkish, Spanish, Hindi, Chinese, and Arabic, making it versatile for a global audience. Its primary goal is to boost both the quality and efficiency of written communication.

UnrealCV is an open-source project designed to bridge computer vision research with the powerful Unreal Engine (UE). It functions as a plugin for UE, extending its capabilities with a set of UnrealCV commands that enable interaction with virtual worlds. This connection facilitates communication between the Unreal Engine environment and external programs like PyTorch or TensorFlow, making it ideal for generating synthetic data for computer vision tasks. Users can either run a compiled game binary with UnrealCV embedded, requiring no prior Unreal Engine knowledge, or install the plugin directly into Unreal Engine to build new virtual worlds using the editor. It supports Unreal Engine 5.6 and offers features like optical flow image capture and calling Blueprint functions from Python.

Talking Buddy: 3D AI Friend, developed by yapAI Labs, offers an immersive mobile experience with hyper-realistic 3D AI companions. This voice-first application provides a judgment-free zone for users to vent, share secrets, and receive emotional support. The AI characters are designed with real 3D emotions, allowing them to smile, listen, and react, moving beyond static images and text. It aims to foster deep human connection through high-fidelity 3D worlds powered by game engines, where voice, emotion, and presence are paramount. The app is iterated directly on mobile, ensuring a seamless experience for users living with AI every day.

ASL_to_English is an open-source project designed to read hand signs and translate them into English words. It leverages the Tensorflow object detection API and a transfer learning model built from a pre-trained ssd_mobilenet model. The tool's dataset is manually created by collecting images from a webcam for specific American Sign Language signs, including "Hello," "I Love You," "Thank you," "Please," "Yes," and "No." Image annotations are performed using the LabelImage tool. This project aims to bridge communication gaps by providing a practical application for ASL translation.

Awesome-LLM-Safety is a comprehensive, curated collection of papers, articles, and various resources specifically focused on the safety aspects of Large Language Models (LLMs). This repository serves as a valuable resource for understanding the safety implications, identifying challenges, and tracking advancements within the LLM domain. It is designed to assist both researchers and practitioners in navigating the complex landscape of LLM safety, offering a centralized hub for relevant information.

awesome-prompts offers a curated collection of ChatGPT prompts, meticulously sourced from the highest-rated GPTs available in the GPTs Store. This resource is specifically designed for individuals interested in prompt engineering, prompt attack, and prompt protection. Beyond just providing prompts, the repository also features a selection of advanced prompt engineering papers, offering deeper insights into the field. Its primary goal is to assist users in discovering, exploring, and understanding effective prompt strategies for various applications.

Lookup is an AI-powered search tool designed to streamline data discovery by unifying searches across various platforms. It leverages artificial intelligence to improve the precision and relevance of search results, helping users find the information they need more efficiently. The tool aims to simplify the process of gathering data from disparate sources into a single, cohesive search experience. Users can explore its capabilities through a free trial.

The domain Quizzio App (quizzio.app) is currently listed for sale. The website content explicitly states, "This domain is for sale. Re-visit the page to contact the owner." There are no active features, services, or information related to an AI tool for quiz creation available on this domain. Visitors are prompted to contact the owner via a form including fields for name, email, message, and website. Therefore, Quizzio App does not function as an AI-powered platform for generating quizzes as its name might suggest, but rather as a placeholder for a domain awaiting purchase.

Awesome-Tabular-LLMs provides a comprehensive, curated list of research papers specifically focused on the application of Large Language Models (LLMs) to various table-related tasks. This resource is designed to keep researchers and practitioners updated on the latest developments in the field. It covers a range of applications, including but not limited to, table question answering, where LLMs interpret and respond to queries based on tabular data; table-to-text generation, which involves converting structured table data into natural language descriptions; and text-to-SQL conversion, enabling users to generate SQL queries from natural language prompts. The primary goal is to serve as a valuable reference for anyone interested in the intersection of LLMs and tabular data processing.

Computer Vision Basics in Microsoft Excel provides a unique approach to understanding computer vision concepts by implementing sample algorithms directly within Microsoft Excel, using only one-liner Excel formulas. This resource aims to demystify computer vision for software developers and others, showcasing how complex tasks like face detection, Hough Transform, and character recognition can be visualized and manipulated within a spreadsheet environment. It leverages a 'surprise trick' to demonstrate and visualize these algorithms without requiring any scripts or third-party plugins. The material is presented through a series of self-explanatory Excel files, making it accessible to those with basic Excel knowledge, even without prior computer vision background. It highlights the continued relevance of classical computer vision techniques for simpler operations and edge devices.

cheatsheet-translation is a collaborative, open-source initiative dedicated to translating cheat sheets related to machine learning, deep learning, and artificial intelligence. The platform's primary goal is to empower users worldwide to comprehend intricate AI concepts in their preferred native languages. It facilitates collaborative translation workflows, significantly enhancing the global accessibility of educational content. Users are encouraged to contribute to the translation and refinement of existing cheat sheets, fostering a community-driven approach to knowledge sharing.