August 1, 2021 Machine Learning

Hierarchical Point Regression for Whole-Body Human Pose Estimation

HPRNet HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation Official PyTroch implementation of HPRNet. HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation,Nermin Samet, Emre Akbas,Under review. (arXiv pre-print) Highlights HPRNet is a bottom-up, one-stage and hierarchical keypoint regression method for whole-body pose estimation. HPRNet has the best performance among bottom-up methods for all the whole-body parts. HPRNet achieves SOTA performance for the face (76.0 AP) and hand (51.2 AP) keypoint estimation. Unlike two-stage methods, HPRNet predicts whole-body pose […]

August 1, 2021 Tracker

Towards End-to-end Video-based Eye Tracking

The code accompanying our ECCV 2020 publication and dataset, EVE. Authors: Seonwook Park, Emre Aksan, Xucong Zhang, and Otmar HilligesProject page: https://ait.ethz.ch/projects/2020/EVE/Codalab (test set evaluation and public leaderboard): https://competitions.codalab.org/competitions/28954 Setup Preferably, setup a Docker image or virtual environment (virtualenvwrapper is recommended) for this repository. Please note that we have tested this code-base in the following environments: Ubuntu 18.04 / A Linux-based cluster system (CentOS 7.8) Python 3.6 / Python 3.7 PyTorch 1.5.1 Clone this repository somewhere with: git clone [email protected]:swook/EVE […]

August 1, 2021 tensorflow

Tensorflow implementation of MIRNet for Low-light image enhancement

MIRNet Tensorflow implementation of the MIRNet architecture as proposed by Learning Enriched Features for Real Image Restoration and Enhancement. Pre-trained Weights Citation @misc{ 2003.06792, Author = {Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang and Ling Shao}, Title = {Learning Enriched Features for Real Image Restoration and Enhancement}, Year = {2020}, Eprint = {arXiv:2003.06792}, } GitHub https://github.com/soumik12345/MIRNet

August 1, 2021 Music

Carousel Personalization in Music Streaming Apps with Contextual Bandits

Carousel Personalization in Music Streaming Apps with Contextual Bandits – RecSys 2020This repository provides Python code and data to reproduce experiments from the article Carousel Personalization in Music Streaming Apps with Contextual Bandits published in the proceedings of the 14th ACM Conference on Recommender Systems (RecSys 2020 – Best Short Paper Candidate). Carousel Personalization Media services providers, such as the music streaming platform Deezer, often leverage swipeable carousels to recommend personalized content to their users. These carousels are ranked lists […]

August 1, 2021 PyTorch

Unofficial PyTorch reimplementation of Hand-Biomechanical-Constraints

Hand Biomechanical Constraints Pytorch Unofficial PyTorch reimplementation of Hand-Biomechanical-Constraints (ECCV2020). This project reimplement following components : 3 kinds of biomechanical soft constraints integrate BMC into training procedure (PyTorch version) Usage Download data Download 3D joint location data joints.zip Google Drive or Baidu Pan (2pip), and . These statistics are from following datasets: Note the data from these datasets under their own licenses. Calculate BMC Run the code python calculate_bmc.py You will get bone_len_max.npy bone_len_min.npy for bone length limits curvatures_max.npy curvatures_min.npy […]

August 1, 2021 Segmentation

Code for weakly supervised segmentation of a single class

SingleClassRL Implementation of weak single object segmentation from paper “Regularized Loss for Weakly Supervised Single Class Semantic Segmentation”, ECCV2020. PDF Main Files train_with_anneal.py: use for training first in annealing stage, then in normal stage train_with_transfer.py: use from training with weight transfer from another dataset, models that can be used for weight transfer are in directory ‘trained_models’ OxfodPet dataset Download OxfordPet from (https://www.robots.ox.ac.uk/~vgg/data/pets/) Files in ‘SingleClassRLdataOxford_iit_petannotations’ should be placed in the ‘annotation’ directory of OxfordPet dataset GitHub https://github.com/morduspordus/SingleClassRL

August 1, 2021 Transformer

Cross Attention in Vision Transformer with python

CAT: Cross Attention in Vision Transformer This is official implement of “CAT: Cross Attention in Vision Transformer”. Abstract Since Transformer has found widespread use in NLP, the potential of Transformer in CV has been realized and has inspired many new approaches. However, the computation required for replacing word tokens with image patches for Transformer after the tokenization of the image is vast(e.g., ViT), which bottlenecks model training and inference. In this paper, we propose a new attention mechanism in Transformer […]

August 1, 2021 Deep Learning

Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals

LapDepth-release This repository is a Pytorch implementation of the paper “Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals” Minsoo Song, Seokjae Lim, and Wonjun Kim*IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) Requirements Python >= 3.7 Pytorch >= 1.6.0 Ubuntu 16.04 CUDA 9.2 cuDNN (if CUDA available) some other packages: geffnet, path, IPython, blessings, progressbar Pretrained models You can download pre-trained model Demo images (Single Test Image Prediction) Make sure you download the pre-trained model and placed it […]

August 1, 2021 Advanced, Libraries, NLP, Python, Text

Why must text data be pre-processed ?

This article was published as a part of the Data Science Blogathon Introduction Language is a structured medium we humans use to communicate with each other. Language can be in the form of speech or text. “Blah blah”, “Meh”, “zzzz…” Yup, we can understand these words. But the question is, “Can computers understand these?” Nop, machines can’t understandthese. In fact, machines can’t understand any text data at all, be it the word “blah” or the word “machine”. They only understand numbers. […]

July 31, 2021 NLP

Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring

July 31, 2021 By: Aitor Ormazabal, Mikel Artetxe, Aitor Soroa, Gorka Labaka, Eneko Agirre Abstract Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings. Such methods critically rely on those embeddings having a similar structure, but it was recently shown that the separate training in different languages causes departures from this assumption. In this paper, we propose an alternative approach that does not have this limitation, while requiring a weak seed dictionary […]

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