Search Results for "fusion"

This project, also known as TorchMultimodal, is a PyTorch library for building, training, and experimenting with multimodal, multi-task models at scale. The library provides modular building blocks such as encoders, fusion modules, loss functions, and transformations that support combining modalities (vision, text, audio, etc.) in unified architectures. It includes a collection of ready model classes—like ALBEF, CLIP, BLIP-2, COCA, FLAVA, MDETR, and Omnivore—that serve as reference implementations you can adopt or adapt. The design emphasizes composability: you can mix and match encoder, fusion, and decoder components rather than starting from monolithic models. ...

This repository contains code for the CVPR2021 paper "Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition".

PoJamas aims to provide a Python and tools for loading, processing, and producing .cr2, pz3 (crz, pzz) files compatible with the SmithMicro (e-frontier) Poser character animation application. PoJamas is composed of: - Python library - Python Wavefront (.obj) 3D viewer based on GLFW - LibreOffice/Python Application (to ease the library and the viewer usage) As of 2020, the project is ported in Python3 As of 2021 this project proposes a 3D viewer for Wavefront files...

...The software enables simulation, control, and analysis of the Makani M600 turbine system, designed to operate offshore and autonomously manage complex aerodynamic behaviors. Built primarily in C++ and Python, the codebase integrates real-time flight control, sensor fusion, aerodynamic modeling, and telemetry visualization. The project also provides comprehensive simulation environments for studying airborne wind power systems and flight dynamics. Although Makani’s commercial development concluded, the software remains valuable for researchers and engineers.

RefineNet is a MATLAB-based framework for semantic image segmentation and general dense prediction tasks. It implements the architecture presented in the CVPR 2017 paper RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation and its extended version published in TPAMI 2019. The framework uses multi-path refinement and improved residual pooling to achieve high-quality segmentation results across multiple benchmark datasets. It provides trained models for datasets...

The IPS Framework has migrated to https://github.com/HPC-SimTools/IPS-framework The IPS framework was developed to support loosely-coupled integrated modeling of fusion plasmas (and was originally the Integrated Plasma Simulator). However, the design is quite general, and is suitable for many scientific and engineering domains. In addition to plasma physics, it is also being used in the engineering of batteries. One of the novel features of the IPS framework is its ability to support parallelism at multiple levels: components can launch individual parallel tasks, and also launch multiple tasks concurrently. ...