Transformer

BoxeR: Box-Attention for 2D and 3D Transformers

By Duy-Kien Nguyen, Jihong Ju, Olaf Booij, Martin R. Oswald, Cees Snoek. This repository is an official implementation of the paper BoxeR: Box-Attention for 2D and 3D Transformers. Introduction TL; DR. BoxeR is a Transformer-based network for end-to-end 2D object detection and instance segmentation, along with 3D object detection. The core of the network is Box-Attention which predicts regions of interest to attend by learning the transformation (translation, scaling, and rotation) from reference windows, yielding competitive performance on several vision […]

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Neural Network

CoNeRF: Controllable Neural Radiance Fields

This is the official implementation for “CoNeRF: Controllable Neural Radiance Fields” The codebase is based on HyperNeRF implemente in JAX, building on JaxNeRF. Setup The code can be run under any environment with Python 3.8 and above. (It may run with lower versions, but we have not tested it). We recommend using Miniconda and setting up an environment: conda create –name conerf python=3.8 Next, install the required packages: pip install -r requirements.txt Install the appropriate JAX distribution    

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Segmentation

Mask Transfiner for High-Quality Instance Segmentation, CVPR 2022

Mask Transfiner for High-Quality Instance Segmentation [Mask Transfiner, CVPR 2022] This is the official pytorch implementation of Transfiner built on the open-source detectron2 [Under Construction]. Mask Transfiner for High-Quality Instance Segmentation Lei Ke, Martin Danelljan, Xia Li, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu CVPR, 2022 Highlights Transfiner: High-quality instance segmentation with state-of-the-art performance and extreme details. Novelty: An efficient transformer targeting for high-resolution instance masks predictions based on the quadtree structure. Efficacy: Large mask and boundary AP improvements on three […]

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End-to-End

End-to-end Point Cloud Correspondences with Transformers

This repository contains the source code for REGTR. REGTR utilizes multiple transformer attention layers to directly predict each downsampled point’s corresponding location in the other point cloud. Unlike typical correspondence-based registration algorithms, the predicted correspondences are clean and do not require an additional RANSAC step. This results in a fast, yet accurate registration. If you find this useful, please cite: @inproceedings{yew2022regtr, title={REGTR: End-to-end Point Cloud Correspondences with Transformers}, author={Yew, Zi Jian and Lee, Gim hee}, booktitle={CVPR}, year={2022}, } Dataset environment […]

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Dataset

Facestar dataset. High quality audio-visual recordings of human conversational speech

Description Existing audio-visual datasets for human speech are either captured in a clean, controlled environment but contain only a small amount of speech data without natural conversations, or are collected in-the-wild with unreliable audio quality, interfering sounds, low face resolution, and unreliable or occluded lip motion. The Facestar dataset aims to enable research on audio-visual modeling in a large-scale and high-quality setting. Core dataset features: 10 hours of high-quality audio-visual speech data audio recordings in a quiet environment at 16kHz […]

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Domain

Pytorch domain library for recommendation systems

Docs TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs. TorchRec contains: Parallelism primitives that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism/model-parallelism. The TorchRec sharder can shard embedding tables with different sharding strategies including data-parallel, table-wise, row-wise, table-wise-row-wise, and column-wise sharding. The TorchRec planner can automatically generate optimized sharding plans for […]

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Python

Python GUI Programming With Tkinter

Python has a lot of GUI frameworks, but Tkinter is the only framework that’s built into the Python standard library. Tkinter has several strengths. It’s cross-platform, so the same code works on Windows, macOS, and Linux. Visual elements are rendered using native operating system elements, so applications built with Tkinter look like they belong on the platform where they’re run. Although Tkinter is considered the de facto Python GUI framework, it’s not without criticism. One notable criticism is that GUIs […]

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Python

Using Python’s datetime Module

Python has several different modules to deal with dates and times. This course concentrates on the primary one, datetime. Dates and times are messy things! Shifts due to daylight savings time and time zones complicate any computing with dates and times. In this course, you’ll tackle that messiness and learn: How to use the datetime module What the zoneinfo module does and how to use it How to calculate the difference between two datetime objects    

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Python

Pagination for a User-Friendly Django App

Django has a Paginator class that comes built in and ready to use. Perhaps you have a project on the go, and you’d like to try out the pagination implementations in the following sections with your app as your base. No problem! However, if you want to follow the step-by-step code examples in this tutorial, then you can download the source code for the Django Python wiki project from the Real Python materials repository: This wiki project contains an app […]

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