Research & Education

awesome-humanoid-robot-learning is a comprehensive GitHub repository that compiles academic papers focused on the field of humanoid robot learning. The collection is meticulously organized by the specific tasks the papers address, making it easy for researchers to find relevant work. A key differentiator of this list is its preference for papers that include real robot experiments, providing a practical and applied perspective. Additionally, papers that offer open-sourced code are highlighted with a star, encouraging reproducibility and further development within the community. This resource is invaluable for academics, researchers, and engineers looking to stay updated on the latest advancements and foundational studies in humanoid robotics and AI.

Awesome-Implicit-NeRF-Robotics is a curated repository offering a comprehensive list of research papers, code implementations, and related websites focused on Implicit Representations and Neural Radiance Fields (NeRF) within the Robotics and Reinforcement Learning (RL) domains. This resource is largely based on the survey paper "Neural Fields in Robotics: A Survey." It categorizes papers into key areas such as Object Pose Estimation, SLAM, Manipulation/RL, Object Reconstruction, Physics, and Planning/Navigation, making it an invaluable resource for academics and practitioners exploring these advanced topics. The repository is actively maintained, with regular updates on new research and workshops in the field.

Awesome-GUI-Agent is a meticulously curated list of papers, projects, and resources specifically focused on multi-modal Graphical User Interface (GUI) agents. This open-source repository serves as a valuable hub for researchers and developers aiming to build advanced digital assistants capable of interacting with computer screens. It categorizes resources into key areas such as Datasets/Benchmarks, Models/Agents, Surveys, and Projects, making it easy to navigate the vast landscape of GUI agent research. The project is actively maintained and encourages contributions, ensuring its relevance and comprehensiveness. It also features an 'Awesome-Paper-Agent' to automatically format arXiv links, streamlining the process of adding new research to the list. This resource is essential for anyone working on or interested in the development of intelligent agents that can understand and operate graphical user interfaces.

Awesome-holistic-3d is a valuable open-source resource for researchers and academics focused on holistic 3D reconstruction in computer vision. This GitHub repository compiles a comprehensive list of relevant papers, datasets, and code, categorized by scene-level and object-level reconstruction. It includes references to tutorials, workshops, and a wide array of research papers spanning from 2009 to 2020. The resource details various datasets with information on the number of scenes, rooms, frames, and annotated structures, making it an essential reference for anyone working on or studying 3D reconstruction techniques.

awesome-self-driving-car is a comprehensive, open-source curated list of resources dedicated to self-driving car technology. It serves as a valuable hub for developers, researchers, and students interested in autonomous vehicles, offering links to full-stack open-source projects like Apollo and Autoware, as well as essential libraries such as ROS, OpenCV, and TensorFlow. The list also includes academic courses from institutions like Udacity and MIT, alongside a vast collection of papers and blogs covering topics from HD mapping and simulation to localization, perception, planning, and control. Furthermore, it details various systems, hardware components, datasets, and benchmarks crucial for autonomous driving research and development.

Awesome-Referring-Image-Segmentation is a curated GitHub repository that compiles a vast collection of academic papers and datasets related to referring image segmentation. This resource is invaluable for researchers and practitioners in the computer vision domain, offering insights into traditional and interactive methods, as well as current challenges in the field. The repository is organized into sections covering datasets, challenges, traditional referring image segmentation, interactive referring image segmentation, referring video object segmentation, 3D referring segmentation, and referring image segmentation in specific domains. It is actively maintained and encourages contributions via pull requests or issue submissions, fostering a collaborative environment for advancing research in this specialized area.

Awesome-RLHF is a comprehensive, open-source repository dedicated to curating resources for Reinforcement Learning with Human Feedback (RLHF). It serves as a vital hub for researchers and practitioners, offering an up-to-date collection of research papers, associated codebases, and relevant datasets. The repository is meticulously organized by publication year, spanning from 2020 to 2025, and includes detailed explanations of RLHF concepts, advanced techniques like Inverse Reinforcement Learning and Human-in-the-Loop RL, and practical examples across various applications such as game playing, recommendation systems, and robotics. Its continuous updates ensure users have access to the latest advancements in the field.

Lidar_For_AD_references is a comprehensive, open-source repository offering a curated list of academic papers and resources focused on LiDAR point cloud processing for autonomous driving applications. This tool is invaluable for researchers and engineers working in the autonomous vehicle domain, providing references across various critical tasks. These tasks include LiDAR point cloud clustering, semantic segmentation, plane extraction, object detection and tracking, registration and localization, feature extraction, and mapping. The repository also covers topics like point cloud density and compression, simulated point clouds, and various LiDAR datasets, making it a central hub for relevant academic literature and practical resources.

PyTorch-RL offers a comprehensive PyTorch implementation of various deep reinforcement learning algorithms. This repository is designed for researchers and developers working with reinforcement learning, providing ready-to-use implementations of popular policy gradient methods such as Trust Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO), and Synchronous A3C (A2C). Additionally, it includes Generative Adversarial Imitation Learning (GAIL). A key feature is its fast Fisher vector product calculation and support for multiprocessing, enabling agents to collect samples from multiple environments simultaneously for improved performance. It supports both discrete and continuous action spaces, making it versatile for different reinforcement learning tasks.

Obsidian PDF++ is an Obsidian.md plugin designed to significantly improve the PDF experience by integrating robust annotation and viewing capabilities directly into the Obsidian environment. It transforms backlinks to PDF files into highlight annotations, allowing users to annotate PDFs simply by linking to text selections. The plugin also supports direct PDF annotation, making highlights visible outside Obsidian, and offers numerous quality-of-life improvements to the built-in PDF viewer. A key differentiator is its approach to sidenotes as pure markdown, ensuring annotations remain accessible even if the plugin is disabled. It also enables Obsidian to function as a standalone PDF annotation tool, facilitating seamless annotation without switching applications.

Trainizi is an award-winning AI solution designed to deliver corporate training on mobile devices. It features a dynamic and adaptive AI instructor that ignites curiosity and critical thinking through relevant, thought-provoking questions. The platform connects with cultural depth by adapting learning content to local cultures, languages, metaphors, and humor. Trainizi delivers media-rich and interactive lessons that auto-adjust to individual learning speeds and styles, boasting a 95% completion rate. This technology empowers instructors to dynamically grow and monetize communities with their edutainment content, making it ideal for enterprises, schools, and communities looking to train a large-scale workforce efficiently.

Lingosnap is an innovative language learning tool designed to make vocabulary acquisition engaging and contextually relevant. By leveraging visual recognition technology, users can simply photograph objects in their environment and instantly receive their names in a chosen new language. This immersive approach helps build vocabulary and improve comprehension in a practical way. The tool focuses on real-world application, allowing learners to connect new words directly with their physical surroundings, fostering a more natural and effective learning experience. It aims to provide an intuitive and interactive method for language learners to expand their linguistic skills.

Awesome-Spatial-Intelligence-in-VLM is a comprehensive, curated list of resources dedicated to spatial intelligence within Vision Language Models (VLMs). This GitHub repository serves as a valuable index for researchers and engineers, bringing together essential methods, datasets, and benchmarks. It aims to facilitate the tracking of the latest advancements in evaluating and enhancing the spatial reasoning capabilities of multimodal models. The list is actively maintained and welcomes contributions, ensuring it remains a current and relevant resource for anyone working on spatial reasoning in VLMs. It categorizes resources into visual-based and text-based methods, as well as visual-based datasets and benchmarks, offering a structured overview of the field.

Creative Biolabs provides custom biotechnology and pharmaceutical services, focusing on the full scope of drug discovery and development. The platform offers extensive services for antibody development projects, including discovery, engineering, and custom production. Key technologies include phage display, yeast display, and single B cell sorting for binder discovery. They also provide services for antibody characterization, immunogenicity analysis, and property optimization like affinity maturation and stability improvement. Additionally, Creative Biolabs offers custom manufacturing for membrane proteins, virus-like particles, and recombinant antibodies, ensuring high-quality solutions for various research and therapeutic needs.

jsfeat is an open-source JavaScript Computer Vision library designed for developers to explore and implement modern computer vision algorithms using JS/HTML5. The library provides a comprehensive set of features, including custom data structures and essential image processing methods such as grayscale conversion, box blur, Gaussian blur, histogram equalization, Canny edges, and various derivative calculations. It also incorporates a Linear Algebra module for LU, Cholesky, and SVD solvers, along with Eigen Vectors and Values. For advanced applications, jsfeat offers a Multiview module with Affine2D and Homography2D motion kernels, and RANSAC/LMEDS motion estimators. Additionally, it includes feature detectors like Fast Corners, YAPE06, YAPE, and ORB, as well as Lucas-Kanade optical flow and HAAR/BBF object detectors, making it a versatile tool for computer vision development.

SegLossOdyssey is an open-source repository offering a comprehensive collection of loss functions specifically designed for medical image segmentation. This tool is invaluable for researchers and practitioners aiming to enhance the accuracy and robustness of their segmentation models, particularly in tasks involving highly imbalanced data. The collection includes implementations in PyTorch and Keras, covering a wide array of loss functions from various research papers and challenges. It highlights the effectiveness of compound loss functions for challenging segmentation tasks and provides a valuable resource for exploring and applying state-of-the-art loss functions in medical imaging.

LIBERO is an open-source benchmarking framework designed for studying knowledge transfer in multitask and lifelong robot learning problems. It provides a procedural generation pipeline capable of creating an infinite number of manipulation tasks, alongside 130 pre-defined tasks grouped into four distinct task suites: LIBERO-Spatial, LIBERO-Object, LIBERO-Goal, and LIBERO-100. These suites are structured to facilitate research into specific types of knowledge transfer, with LIBERO-100 focusing on entangled knowledge transfer for pretraining and testing lifelong learning performance. The framework also includes five research topics, three visuomotor policy network architectures, and three lifelong learning algorithms, along with sequential finetuning and multitask learning baselines. High-quality human teleoperation demonstrations are available for all task suites.

Learnable-triangulation-pytorch is an official PyTorch implementation of the paper "Learnable Triangulation of Human Pose" (ICCV 2019, oral). This open-source project focuses on 3D human pose estimation from multiple cameras, offering two novel methods: Algebraic and Volumetric learnable triangulation. These methods significantly outperform previous state-of-the-art techniques, with the Volumetric model achieving a 2.4 times reduction in error. The repository provides code for training and evaluation, supports both single and multi-GPU setups, and includes pretrained models and configurations for the Human3.6M dataset. It is designed for researchers and engineers working on advanced computer vision tasks, particularly in human pose estimation.

data-pipelines-with-apache-airflow is a GitHub repository containing code examples designed to accompany the Manning book 'Data Pipelines with Apache Airflow'. The repository is meticulously structured, with dedicated directories for each chapter of the book, making it easy for users to follow along and implement the concepts discussed. Each chapter's directory typically includes Airflow DAG examples, a docker-compose.yml file for setting up the necessary containers and an Airflow instance, and a chapter-specific readme for detailed instructions. This resource is ideal for individuals looking to learn and practice building data pipelines with Apache Airflow, providing practical, runnable code to reinforce theoretical knowledge.

darknet_ros is a ROS (Robot Operating System) package designed for real-time object detection in camera images, leveraging the You Only Look Once (YOLO) system. It supports YOLO V3 on both GPU and CPU, offering significant speed advantages with CUDA-enabled GPUs. The package comes with pre-trained models capable of detecting objects from VOC and COCO datasets, and also allows users to train and deploy networks with their own custom detection objects. It provides ROS-related parameters for configuring publishers, subscribers, and actions, making it highly adaptable for robotics applications. The tool is open-source and actively maintained by leggedrobotics, providing a robust solution for integrating advanced object detection into robotic systems.

MedMamba is the official code repository for "MedMamba: Vision Mamba for Medical Image Classification." This innovative tool addresses the limitations of traditional CNNs and ViTs in medical image analysis by introducing a novel hybrid basic block called SS-Conv-SSM. This block effectively integrates convolutional layers for local feature extraction with State Space Models (SSMs) to capture long-range dependencies, ensuring efficient modeling of medical images from diverse modalities. MedMamba is designed to provide fewer model parameters and a lower computational burden without sacrificing accuracy, making it suitable for real-world applications with limited computational resources. It has been extensively tested across 16 datasets, ten imaging modalities, and over 400,000 images, demonstrating competitive performance in classifying various medical images.

mega.pytorch offers an official PyTorch implementation of the "Memory Enhanced Global-Local Aggregation for Video Object Detection" (MEGA) approach, which was accepted by CVPR 2020. This repository is built upon maskrcnn_benchmark and includes training scripts to replicate results on ImageNet VID. Beyond MEGA, it also implements other video object detection algorithms like FGFA and RDN, welcoming contributions for new methods. The project aims to support further research in video object detection, providing pretrained models and detailed instructions for installation, data preparation, inference, and training.

Thai Ai is a mobile application designed to help users learn Thai through personalized AI tutoring. The platform offers interactive audio lessons covering a wide range of topics, from daily life to business communication. Users can practice their Thai skills in realistic conversation scenarios with AI tutors, who can be customized by personality, accent, and region. The app provides powerful learning tools such as real-time corrections, message translation, pronunciation assessment, and bookmarking to enhance the learning experience and help users achieve fluency. It aims to move beyond traditional textbook learning by focusing on practical, conversational skills for everyday situations.

dev-conf-replay is an open-source repository that serves as a comprehensive collection of replay links for recent IT seminars and developer conferences in Korea. It organizes video recordings from various events, including those hosted by major IT companies like Naver, Kakao, Line, and Samsung, as well as specialized conferences on AI, Big Data, Cloud, DevOps, Blockchain, Mobile, and Programming Languages. This tool is designed to help developers and IT professionals easily access educational content, stay informed about the latest industry trends, and review past conference sessions at their convenience. The repository is regularly updated with new videos and categorized for easy navigation.