Research & Education

Latex Ocr is a specialized tool engineered to transform images containing mathematical formulas and equations directly into Latex code. This functionality is particularly beneficial for users who frequently work with academic or scientific documents. By enabling the extraction and digitization of complex mathematical expressions from visual sources, Latex Ocr streamlines the process of incorporating these elements into Latex-based projects. It serves as a valuable resource for individuals in educational and research fields.

Keye VL 8B Preview is an AI chatbot that specializes in visual question answering and image captioning. This tool is built to understand and process multimodal inputs, allowing users to interact with images by asking questions and generating descriptive captions. It leverages advanced AI models to interpret visual information and provide relevant textual responses. The tool is available for free, making it accessible for various applications requiring visual AI capabilities.

LD T3D is an AI-powered tool that facilitates the creation of 3D models. It leverages artificial intelligence to generate 3D assets, streamlining the modeling process for its users. The tool is accessible for free on the Hugging Face platform, making it a valuable resource for individuals and professionals looking to integrate AI into their 3D design workflows. It caters to a diverse audience, including 3D artists, game developers, and AI researchers who require efficient 3D asset generation capabilities.

LightDiffusion-Next is an AI-powered tool designed for generating images using advanced diffusion models. Hosted on Hugging Face, it provides a platform for users to experiment with AI image creation. The tool is accessible to a broad audience, including AI enthusiasts and individuals interested in digital image creation, offering a straightforward way to produce visual content.

Manticore 13B Chat is an AI chatbot tool specifically created for the development and testing of AI chatbots and language models. It provides a platform for users to explore and experiment with conversational AI technologies. The tool is particularly useful for individuals and organizations engaged in researching the nuances of AI-driven conversations. Manticore 13B Chat is offered at no cost, making it accessible for a wide range of users interested in AI chatbot development.

Lp Music Caps is an AI tool accessible via Hugging Face, focusing on various music-related applications. While specific functionalities are not explicitly detailed, its design suggests capabilities in areas such as music generation, composition, or analytical tasks. The tool aims to provide AI-powered assistance for users working with music, leveraging the robust infrastructure of Hugging Face. It is currently offered without cost, making it an accessible option for individuals interested in exploring AI's potential in music.

MiniCPM-V-4 5-Demo provides an interactive platform on Hugging Face for users to engage with the MiniCPM-V-4 5 AI model. This chatbot serves as a demonstration tool, enabling individuals to explore and evaluate the model's capabilities through direct interaction. It is offered without cost, making it an accessible resource for those involved in research, education, or simply curious about the performance of the MiniCPM-V-4 5 model.

Free Academic Tools is a platform designed to assist students with their academic endeavors by providing free AI-powered tools. The platform focuses on supporting key aspects of academic work, including research, the creation of citations, and the checking of references. Its primary goal is to eliminate financial barriers, such as paywalls, making essential research support readily accessible to all students.

Mamba_State_Space_Model_Paper_List is an open-source, curated list of research papers focused on State-Space Models and Mamba. Mamba is presented as a new-generation network alternative to Transformers. This resource, maintained on GitHub, provides a comprehensive collection of papers that delve into both the theoretical foundations and practical applications of Mamba models. It is specifically designed to be a valuable asset for researchers and practitioners working within this specialized field.

CycleISP is an advanced image restoration framework presented at CVPR 2020, designed to address the limitations of traditional image denoising methods that rely on synthetic data with additive white Gaussian noise. This tool models the complex camera imaging pipeline in both forward and reverse directions, enabling the generation of realistic image pairs for denoising in both RAW and sRGB formats. By training a new image denoising network on this realistic synthetic data, CycleISP achieves state-of-the-art performance on real camera benchmark datasets. A key differentiator is its efficiency, with approximately five times fewer parameters than previous leading methods for RAW denoising. Beyond denoising, the framework demonstrates versatility, for example, in color matching for stereoscopic cinema.

DirectVoxGO is an open-source tool designed for fast radiance field reconstruction, leveraging direct voxel grid optimization. It significantly speeds up NeRF (Neural Radiance Fields) by replacing traditional MLPs with a voxel grid for volume densities and a dense feature grid with a shallow MLP for view-dependent colors. The tool includes a PyTorch CUDA extension for additional 2-3x speedup and an O(N) realization for the distortion loss, improving both training time and quality. It supports various datasets including bounded and unbounded inward-facing scenes, as well as forward-facing scenes, making it versatile for researchers and engineers in computer vision.

DeepGTAV is an open-source plugin designed for Grand Theft Auto V, converting the popular game into a sophisticated research environment for vision-based self-driving cars. This tool enables researchers to simulate and test AI models for autonomous navigation and computer vision tasks directly within the game's dynamic world. Users can configure various environmental parameters such as weather, time, vehicle type, and driving style, as well as control data transmission rates and frame dimensions. DeepGTAV facilitates sending driving commands to control vehicles and receiving rich data streams in JSON format, making it an invaluable resource for developing and evaluating self-driving agents and generating extensive datasets.

HoloLens2ForCV offers sample code and comprehensive documentation for researchers looking to leverage the Microsoft HoloLens 2 for computer vision applications. This tool facilitates access to the HoloLens 2's Research Mode API, allowing users to tap into raw sensor streams such as depth cameras, gray-scale cameras, and the Inertial Measurement Unit (IMU). It includes various sample apps like CalibrationVisualization, CameraWithCVAndCalibration (using OpenCV for ArUco marker detection), SensorVisualization, and StreamRecorder for capturing and post-processing data. The project aims to support and extend the use of HoloLens 2 as a powerful device for robotics and computer vision research, welcoming contributions from the academic community.

ML-GCN is a PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, as presented in a CVPR 2019 paper. This open-source project provides researchers and developers with the code and pre-trained models necessary to apply GCNs to multi-label image recognition tasks. The implementation highlights improvements achieved by replacing Global Average Pooling (GAP) with Global Max Pooling (GMP) for feature aggregation, demonstrating enhanced performance on datasets like COCO, NUS-WIDE, and VOC2007. It includes detailed instructions for setting up requirements, downloading models, and running demos for VOC 2007 and COCO 2014 datasets, making it a valuable resource for academic research and practical application in computer vision.

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.

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.

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.

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.

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.

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.

SearchGPTool is an AI-powered search tool designed to deliver more personalized and accurate search results. By leveraging artificial intelligence, it aims to significantly improve the overall search experience for users. The tool is offered for free, making advanced search capabilities accessible to a broad audience. Its core function revolves around refining search outcomes to be more relevant to individual user needs, moving beyond traditional search engine limitations.

Deep3DFaceRecon_pytorch is an open-source PyTorch implementation for accurate 3D face reconstruction, building upon the original TensorFlow version. It utilizes weakly-supervised learning to reconstruct 3D faces from single images or image sets, offering improved accuracy and visual consistency. Key enhancements include a differentiable renderer using Nvdiffrast, Arcface for perceptual loss computation, and data augmentation during training. The tool achieves state-of-the-art performance on various datasets like FaceWarehouse, MICC Florence, and the NoW Challenge. It supports both inference with pre-trained models and training new models from scratch, making it suitable for researchers and developers in computer vision and 3D modeling.