🔦 YOLOV5-ti-lite Object Detection Models

Code: https://github.com/texasinstruments/edgeai-yolov5

Paper: https://arxiv.org/abs/2204.06806v1

Dataset: https://paperswithcode.com/dataset/coco

@ArtificialIntelligencedl

✏️ Sat2lod2: A Software For Automated Lod-2 Modeling From Satellite-Derived Orthophoto And Digital Surface Model

The proposed method starts building detection results through a deep learning-based detector and vectorizes individual segments into polygons using a “three-step” polygon extraction method

Github: https://github.com/gdaosu/lod2buildingmodel

Paper: https://arxiv.org/abs/2204.04139v1

Datasett: https://drive.google.com/file/d/1rA7SRPbSYFJwOBc7IfXxBgmUroTOZIOF/view?usp=sharing

Video: https://youtu.be/Nn4OABsEOXk

@ai_machinelearning_big_data

🔎 CenterNet++ for Object Detection

Code: https://github.com/Duankaiwen/PyCenterNet

Paper: https://arxiv.org/abs/2204.08394v1

Dataset: https://paperswithcode.com/dataset/coco

✏️ Sat2lod2: A Software For Automated Lod-2 Modeling From Satellite-Derived Orthophoto And Digital Surface Model

The proposed method starts building detection results through a deep learning-based detector and vectorizes individual segments into polygons using a “three-step” polygon extraction method

Github: https://github.com/jingkang50/openood

Paper: https://arxiv.org/abs/2204.05306v1

Dataset: https://entuedu-my.sharepoint.com/:f:/g/personal/jingkang001_e_ntu_edu_sg/Eso7IDKUKQ9AoY7hm9IU2gIBMWNnWGCYPwClpH0TASRLmg?e=iEYhXO

More tutorials: https://github.com/Jingkang50/OpenOOD/wiki/Get-Started

@ai_machinelearning_big_data

Understanding Engagement from Video Screengrabs

Code: https://github.com/wanghewei16/video-engagement-analysis

Paper: https://arxiv.org/abs/2204.06454v1

The data source: https://github.com/e-drishti/wacv2016.


@ai_machinelearning_big_data

✔️ Localization Distillation for Dense Object Detection

Github: https://github.com/HikariTJU/LD

Paper: https://arxiv.org/abs/2102.12252

Datasett: https://paperswithcode.com/dataset/dota


@ai_machinelearning_big_data

OSSO: Obtaining Skeletal Shape from Outside (CVPR 2022)

Given a body shape with SMPL or STAR topology (in blue), we infer the underlying skeleton (in yellow).

Code: https://arxiv.org/abs/2204.10129v1

Paper: https://arxiv.org/abs/2204.09838v1

Dataset: https://paperswithcode.com/dataset/agora

@ArtificialIntelligencedl

🔩 Glass Segmentation with RGB-Thermal Image Pairs

Github: https://github.com/dong-huo/rgb-t-glass-segmentation

Paper: https://arxiv.org/abs/2204.05453v2

Datasett: https://drive.google.com/file/d/1ysG04qGmnZv7UaybZUuyybaJYJLUkNHX/view?usp=sharing


@ai_machinelearning_big_data

Яндекс открывает резидентскую программу по машинному обучению ML Residency.

Ее участники будут проводить исследования и эксперименты в ML, писать научные работы и посещать ведущие конференции.

Подать заявку могут как студенты и аспиранты вузов, так и опытные специалисты в профильных областях: математике, физике, компьютерных науках. Работа в проекте оплачивается.

Узнать подробнее можно здесь.

🌟 Parametric Distributionally Robust Optimization

Github: https://github.com/pmichel31415/P-DRO

Paper: https://arxiv.org/abs/2204.06340v1

Datasett: https://paperswithcode.com/dataset/celeba

@ai_machinelearning_big_data

📲 Deep-significance: Easy and Better Significance Testing for Deep Neural Networks

Github: https://github.com/Kaleidophon/deep-significance

Paper: https://arxiv.org/abs/2204.06815v1

Examples: https://github.com/Kaleidophon/deep-significance#examples

@ai_machinelearning_big_data

🤖 Transformer-Rankers

Transformer-rankers is a library to conduct ranking experiments with transformers.


Code: https://github.com/Guzpenha/transformer_rankers

Paper: https://arxiv.org/abs/2204.10558v1

Colab: https://colab.research.google.com/drive/1wGmaO3emC7Sg-tA7nGehIQ2vjOLN9S5e?usp=sharing

👁 An Extendable, Efficient and Effective Transformer-based Object Detector

Github: https://github.com/naver-ai/vidt

Paper: https://arxiv.org/abs/2204.07962v1

Dataset: https://paperswithcode.com/dataset/coco

@ai_machinelearning_big_data

🌐 DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings


Code: https://github.com/voidism/diffcse

Paper: https://arxiv.org/abs/2204.10298v1

Dataset: https://paperswithcode.com/dataset/sst

@ai_machinelearning_big_data

🌄 NAFSSR: Stereo Image Super-Resolution Using NAFNet

Github: https://github.com/megvii-research/NAFNet

Paper: https://arxiv.org/abs/2204.08714v1

Dataset: https://paperswithcode.com/dataset/kitti

Demo: https://colab.research.google.com/drive/1dkO5AyktmBoWwxBwoKFUurIDn0m4qDXT?usp=sharing

@ai_machinelearning_big_data

🛠 MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment

Github: https://github.com/iigroup/maniqa

Paper: https://arxiv.org/abs/2204.08958v1

Dataset: https://paperswithcode.com/dataset/kitti

Demo: https://paperswithcode.com/dataset/pipal-perceptual-iqa-dataset

@ai_machinelearning_big_data

🚰 Simulating Fluids in Real-World Still Images

surface-based layered representation(SLR), which decomposes the fluid and the static objects in the scene, to better synthesize the animated videos from a single fluid imagе

Code: https://github.com/generalizable-neural-performer/gnr

Paper: http://arxiv.org/abs/2204.11335

Project: https://simulatingfluids.github.io/

🚀 TorchSparse: Efficient Point Cloud Inference Engine

Github: https://github.com/mit-han-lab/torchsparse

Paper: https://arxiv.org/abs/2204.10319v1

Dataset: https://paperswithcode.com/dataset/nuscenes

Demo: https://paperswithcode.com/dataset/pipal-perceptual-iqa-dataset

@ai_machinelearning_big_data

Мониторинги показывают, что GPU загружены, при этом видно, что они не потребляют электричества. Что делать? Хорошая статья с ответом на этот вопрос.

Будет полезно почитать, даже если вы занимаетесь обучением моделек на домашних GPU.

🔹 Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval

Github: https://github.com/xiaoyuan1996/AMFMN

Paper: https://arxiv.org/abs/2204.09868v1

Dataset: https://paperswithcode.com/dataset/kitti

@ai_machinelearning_big_data