▶️ Deepminds's Perceiver: General Perception with Iterative Attention

General architecture that works on many kinds of data

https://deepmind.com/blog/article/building-architectures-that-can-handle-the-worlds-data

Github: https://github.com/deepmind/deepmind-research/tree/master/perceiver

Colab: https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/perceiver/colabs/masked_language_modelling.ipynb

Paper: https://arxiv.org/abs/2103.03206

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📸 Physics-based Noise Modeling for Extreme Low-light Photography

Github: https://github.com/Vandermode/ELD

Paper: https://arxiv.org/abs/2108.02158v1

Dataset: https://drive.google.com/drive/folders/1CT2Ny9W9ArdSQaHNxC5hGwav9lZHoqJa?usp=sharing

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👁 Improving Contrastive Learning by Visualizing Feature Transformation

Github: https://github.com/DTennant/CL-Visualizing-Feature-Transformation

Paper: https://arxiv.org/abs/2108.02982

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📹 Internal Video Inpainting by Implicit Long-range Propagation

Github: https://github.com/Tengfei-Wang/Implicit-Internal-Video-Inpainting

Paper: https://arxiv.org/abs/2108.01912v1

4k Data: https://github.com/Tengfei-Wang/Annotated-4K-Videos

Dataset: https://paperswithcode.com/dataset/videoremoval4k

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🔍 Contrastive Sensor Fusion

Github: https://github.com/descarteslabs/contrastive_sensor_fusion

Paper: https://arxiv.org/abs/2108.05094v1

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🎨 Paint Transformer: Feed Forward Neural Painting with Stroke Prediction

Github: https://github.com/huage001/painttransformer

Paper: https://arxiv.org/abs/2108.03798

Paddle Implementation: https://github.com/PaddlePaddle/PaddleGAN

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🧹 Overview of data cleaning methods

https://habr.com/ru/company/ru_mts/blog/571606/

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⚪ SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation (SIGGRAPH 2021)

Github: https://github.com/liruihui/sp-gan

Paper: https://arxiv.org/abs/2108.04476v1

Project: https://liruihui.github.io/publication/SP-GAN/

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ESPnet: end-to-end speech processing toolkit

Github: https://github.com/espnet/espnet

Paper: https://arxiv.org/abs/2107.09011v2

Notebook: https://github.com/espnet/notebook

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Multiscale Vision Transformers: A hierarchical architecture for representing image and video information

Facebook blog: https://ai.facebook.com/blog/multiscale-vision-transformers-an-architecture-for-modeling-visual-data/

Code: https://github.com/facebookresearch/SlowFast

Pytorch video: https://github.com/facebookresearch/pytorchvideo

Paper: https://arxiv.org/abs/2104.11227

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🖊 Как внедряются элементы машинного обучения в службу поддержки: об этом рассказал Яндекс Go в новой статье на VC.

Внутри можно узнать, каких результатов добилась компания и какие задачи решила благодаря SupportAI:

VC: https://vc.ru/yandex.go/280385-yandeks-uluchshil-kachestvo-pismennoy-podderzhki-polzovateley-i-sokratil-rashody-bolee-chem-na-45

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Oriented R-CNN for Object Detection

Github: https://github.com/jbwang1997/OBBDetection

BboxToolkit: https://github.com/jbwang1997/BboxToolkit

Paper: https://arxiv.org/abs/2104.11227

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⏺ Towards Interpretable Deep Metric Learning with Structural Matching

Github: https://github.com/wl-zhao/diml

Paper: https://arxiv.org/abs/2108.05889v1

RevisitDML: https://github.com/wl-zhao/diml

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👁 MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding

MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition

Github: https://github.com/open-mmlab/mmocr

Paper: https://arxiv.org/abs/2108.06543v1

Documentation: https://mmocr.readthedocs.io/en/latest/

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💡 X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics

Github: https://github.com/yehli/xmodaler

Paper: https://arxiv.org/abs/2108.08217v1

Project: https://xmodaler.readthedocs.io/en/latest/

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✔️ Fully Convolutional Networks for Panoptic Segmentation

Github: https://github.com/dvlab-research/panopticfcn

Paper: https://arxiv.org/pdf/2012.00720.pdf

Methods: https://paperswithcode.com/method/fcn

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🔍 TOOD: Task-aligned One-stage Object Detection

Github: https://github.com/fcjian/TOOD

Paper: https://arxiv.org/abs/2108.07755v2

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

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🎩 Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval

Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages.

Github: https://github.com/castorini/mr.tydi

Paper: https://arxiv.org/abs/2108.08787

Tasks: https://paperswithcode.com/task/representation-learning

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Data-driven организация: когда алгоритмы заменят людей?

В Data-Driven подходе все принимаемые решения основаны на big data. Что это — просто мода на использование больших данных или действительно эффективный инструмент? А может, всего лишь хайповое название привычных вещей?

Этой теме будет посвящен «Цифровой четверг» Х5.

Дата: 30 сентября 10:00, онлайн

Открытый разговор с экспертами рынка (X5 Group, Ростелеком, Яндекс.Музыка, Кинопоиск, НИУ ВШЭ, Segmento) о том, как и зачем становиться Data-Driven компанией, почему иногда кейсы работы с данными не приносят реальной пользы бизнесу и могут ли алгоритмы заменить людей?

Участие бесплатное, но необходима регистрация. Подробнее про мероприятие тоже по ссылке.

🔗 A Unified Objective for Novel Class Discovery

Github: https://github.com/DonkeyShot21/UNO

Paper: https://arxiv.org/abs/2108.08536v2

Dataset: https://paperswithcode.com/dataset/cifar-100

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