🐚 Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers
Github: https://github.com/lkeab/BCNet
Paper: https://arxiv.org/abs/2208.04438v1
Dataset: https://paperswithcode.com/dataset/bdd100k
Video: https://www.youtube.com/watch?v=iHlGJppJGiQ
@ai_machinelearning_big_data
Github: https://github.com/lkeab/BCNet
Paper: https://arxiv.org/abs/2208.04438v1
Dataset: https://paperswithcode.com/dataset/bdd100k
Video: https://www.youtube.com/watch?v=iHlGJppJGiQ
@ai_machinelearning_big_data
👍8🔥2
📲LAMDA-SSL: Semi-Supervised Learning in Python
Github: https://github.com/ygzwqzd/lamda-ssl
Paper: https://arxiv.org/pdf/2208.04610.pdf
Docs: https://ygzwqzd.github.io/LAMDA-SSL
@ai_machinelearning_big_data
Github: https://github.com/ygzwqzd/lamda-ssl
Paper: https://arxiv.org/pdf/2208.04610.pdf
Docs: https://ygzwqzd.github.io/LAMDA-SSL
@ai_machinelearning_big_data
👍13🔥2👎1
🎼 ROC: A New Paradigm for Lyric-to-Melody Generation
Muzic is a research project on AI music that empowers music understanding and generation with deep learning and artificial intelligence.
Github: https://github.com/microsoft/muzic
Paper: https://arxiv.org/abs/2208.05697v1
Project: https://www.microsoft.com/en-us/research/project/ai-music/
@ai_machinelearning_big_data
Muzic is a research project on AI music that empowers music understanding and generation with deep learning and artificial intelligence.
Github: https://github.com/microsoft/muzic
Paper: https://arxiv.org/abs/2208.05697v1
Project: https://www.microsoft.com/en-us/research/project/ai-music/
@ai_machinelearning_big_data
👍17🔥6❤2
🗣 Speech Enhancement and Dereverberation with Diffusion-based Generative Models
Github: https://github.com/sp-uhh/sgmse
Paper: https://arxiv.org/abs/2208.05830v1
Pretrained checkpoints: https://drive.google.com/drive/folders/1CSnkhUSoiv3RG0xg7WEcVapyLuwDaLbe?usp=sharing
@ai_machinelearning_big_data
Github: https://github.com/sp-uhh/sgmse
Paper: https://arxiv.org/abs/2208.05830v1
Pretrained checkpoints: https://drive.google.com/drive/folders/1CSnkhUSoiv3RG0xg7WEcVapyLuwDaLbe?usp=sharing
@ai_machinelearning_big_data
🔥10👍3
🧔 StyleFaceV - Official PyTorch Implementation
StyleFaceV produces high-fidelity identity-preserving face videos with vivid movements
Github: https://github.com/arthur-qiu/stylefacev
Project: http://haonanqiu.com/projects/StyleFaceV.html
Video: https://youtu.be/BZNLcD04-Fc
Paper: https://arxiv.org/abs/2208.07862v1
Dataset: https://paperswithcode.com/dataset/faceforensics-1
@ai_machinelearning_big_data
StyleFaceV produces high-fidelity identity-preserving face videos with vivid movements
Github: https://github.com/arthur-qiu/stylefacev
Project: http://haonanqiu.com/projects/StyleFaceV.html
Video: https://youtu.be/BZNLcD04-Fc
Paper: https://arxiv.org/abs/2208.07862v1
Dataset: https://paperswithcode.com/dataset/faceforensics-1
@ai_machinelearning_big_data
🔥12👍3
🎆 Unifying Visual Perception by Dispersible Points Learning
Conceptually simple, flexible, and universal visual perception head for variant visual task
Github: https://github.com/sense-x/unihead
Paper: https://arxiv.org/abs/2208.08630v1
Model: https://drive.google.com/file/d/1TwFCog_PMd1HWA7s-s9pN2F_fgyMyR3x/view
Datasets: https://paperswithcode.com/dataset/imagenet
@ai_machinelearning_big_data
Conceptually simple, flexible, and universal visual perception head for variant visual task
Github: https://github.com/sense-x/unihead
Paper: https://arxiv.org/abs/2208.08630v1
Model: https://drive.google.com/file/d/1TwFCog_PMd1HWA7s-s9pN2F_fgyMyR3x/view
Datasets: https://paperswithcode.com/dataset/imagenet
@ai_machinelearning_big_data
👍12🔥3
⚙️ Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
Github: https://github.com/arpitbansal297/cold-diffusion-models
Paper: https://arxiv.org/abs/2208.09392v1
Cold-Diffusion: https://arxiv.org/abs/2208.09392
Datasets: https://paperswithcode.com/dataset/celeba
@ai_machinelearning_big_data
Github: https://github.com/arpitbansal297/cold-diffusion-models
Paper: https://arxiv.org/abs/2208.09392v1
Cold-Diffusion: https://arxiv.org/abs/2208.09392
Datasets: https://paperswithcode.com/dataset/celeba
@ai_machinelearning_big_data
👍10🐳4🔥3
🔥 Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
Masked "language" modeling on images (Imglish), texts (English), and image-text pairs ("parallel sentences") in a unified manner.
Github: https://github.com/microsoft/unilm/tree/master/beit
Paper: https://arxiv.org/abs/2208.10442v1
Datasets: https://paperswithcode.com/dataset/visual-genome
@ai_machinelearning_big_data
Masked "language" modeling on images (Imglish), texts (English), and image-text pairs ("parallel sentences") in a unified manner.
Github: https://github.com/microsoft/unilm/tree/master/beit
Paper: https://arxiv.org/abs/2208.10442v1
Datasets: https://paperswithcode.com/dataset/visual-genome
@ai_machinelearning_big_data
🔥12👍6😱4👏1
✅ Awesome-Dataset-Distillation
Github: https://github.com/Guang000/Awesome-Dataset-Distillation
Awesome Computer Vision: https://github.com/jbhuang0604/awesome-computer-vision
Paper: https://arxiv.org/abs/2208.11311v1
Datasets: https://paperswithcode.com/dataset/cifar-10
@ai_machinelearning_big_data
Github: https://github.com/Guang000/Awesome-Dataset-Distillation
Awesome Computer Vision: https://github.com/jbhuang0604/awesome-computer-vision
Paper: https://arxiv.org/abs/2208.11311v1
Datasets: https://paperswithcode.com/dataset/cifar-10
@ai_machinelearning_big_data
👍20🔥5❤3
🥇 The Complete Data Science Study Roadmap
➡️ Read
🎲 Statistics Fundamental by Josh Starmer
👨🎓 CS229 machine learning Stanford
@ai_machinelearning_big_data
➡️ Read
🎲 Statistics Fundamental by Josh Starmer
👨🎓 CS229 machine learning Stanford
@ai_machinelearning_big_data
👍19🔥2🕊1
⭐️ YOLOX-PAI: An Improved YOLOX Version by PAI
Github: https://github.com/alibaba/EasyCV
Paper: https://arxiv.org/abs/2208.13040v1
Datasets: https://paperswithcode.com/dataset/coco
@ai_machinelearning_big_data
Github: https://github.com/alibaba/EasyCV
Paper: https://arxiv.org/abs/2208.13040v1
Datasets: https://paperswithcode.com/dataset/coco
@ai_machinelearning_big_data
👍10🔥1
This media is not supported in your browser
VIEW IN TELEGRAM
🔋 Self-Supervised Pyramid Representation Learning
for Multi-Label Visual Analysis and Beyond
Github: https://github.com/wesleyhsieh0806/ss-prl
Paper: https://arxiv.org/abs/2208.14439v1
Datasets: https://github.com/wesleyhsieh0806/ss-prl#books-prepare-dataset
Downstream: http://host.robots.ox.ac.uk/pascal/VOC/
@ai_machinelearning_big_data
for Multi-Label Visual Analysis and Beyond
Github: https://github.com/wesleyhsieh0806/ss-prl
Paper: https://arxiv.org/abs/2208.14439v1
Datasets: https://github.com/wesleyhsieh0806/ss-prl#books-prepare-dataset
Downstream: http://host.robots.ox.ac.uk/pascal/VOC/
@ai_machinelearning_big_data
🔥7👍3
🖼 PyTorch Image Quality (PIQ) is a collection of measures and metrics for image quality assessment.
$ pip install piq
Github: https://github.com/photosynthesis-team/piq
Paper: https://arxiv.org/abs/2208.14818v1
Docs: https://piq.readthedocs.io.
Datasets: https://paperswithcode.com/dataset/kadid-10k
@ai_machinelearning_big_data
$ pip install piq
Github: https://github.com/photosynthesis-team/piq
Paper: https://arxiv.org/abs/2208.14818v1
Docs: https://piq.readthedocs.io.
Datasets: https://paperswithcode.com/dataset/kadid-10k
@ai_machinelearning_big_data
🔥12👍8🤩1
Как организовать потоковую обработку данных. Часть 2!
В первой части Евгений Ненахов из центра Big Data МТС Digital рассказал об основных компонентах методологии, а сейчас — о том, как ими пользоваться.
Из новой статьи вы узнаете:
➖ где хранить конфигурации
➖ как настроить Kafka и Spark Streaming
➖ как снизить нагрузку на GC и многое другое
О том, как создать универсальный инструмент потоковой обработки данных и построить с его помощью мощную систему стриминга, способную обрабатывать 7 млн событий в пике, читайте в блоге МТС на Хабре.
В первой части Евгений Ненахов из центра Big Data МТС Digital рассказал об основных компонентах методологии, а сейчас — о том, как ими пользоваться.
Из новой статьи вы узнаете:
➖ где хранить конфигурации
➖ как настроить Kafka и Spark Streaming
➖ как снизить нагрузку на GC и многое другое
О том, как создать универсальный инструмент потоковой обработки данных и построить с его помощью мощную систему стриминга, способную обрабатывать 7 млн событий в пике, читайте в блоге МТС на Хабре.
Хабр
Как организовать потоковую обработку данных. Часть 1
Привет, Хабр! Меня зовут Евгений Ненахов, я работаю в центре Big Data МТС Digital . В этой статье я расскажу о том, как мы создали универсальный инструмент потоковой обработки данных и построили с его...
👍11❤4
🏙 2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC Videos
Github: https://github.com/atelili/2bivqa
Paper: https://arxiv.org/abs/2208.14774v1
Dataset: https://paperswithcode.com/dataset/live-vqc
Tasks: https://paperswithcode.com/task/video-quality-assessment
@ai_machinelearning_big_data
Github: https://github.com/atelili/2bivqa
Paper: https://arxiv.org/abs/2208.14774v1
Dataset: https://paperswithcode.com/dataset/live-vqc
Tasks: https://paperswithcode.com/task/video-quality-assessment
@ai_machinelearning_big_data
👍9🔥2
This media is not supported in your browser
VIEW IN TELEGRAM
@python_job_interview - здесь собраны все возможные вопросы и ответы с реальных Python собеседований.
@golang_interview - пройти Golang собеседование.
@machinelearning_interview - канал подготовит к собеседованию по машинному обучению и алгоритмам .
@data_analysis_ml - самая востребованная Python профессия.
@golang_interview - пройти Golang собеседование.
@machinelearning_interview - канал подготовит к собеседованию по машинному обучению и алгоритмам .
@data_analysis_ml - самая востребованная Python профессия.
👍10
This media is not supported in your browser
VIEW IN TELEGRAM
🤖 Transformers are Sample Efficient World Models
New state of the art for methods without lookahead search, and even surpasses MuZero.
⚙️ Github
➡️ Paper
💻Dataset
@ai_machinelearning_big_data
New state of the art for methods without lookahead search, and even surpasses MuZero.
⚙️ Github
➡️ Paper
💻Dataset
@ai_machinelearning_big_data
👍18🔥3
🔵 Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces
The library features (approximate) computational techniques for heat and Matérn kernels on compact Lie groups.
⚙️ Github
➡️ Paper
⚪ SphericalHarmonics
@ai_machinelearning_big_data
The library features (approximate) computational techniques for heat and Matérn kernels on compact Lie groups.
⚙️ Github
➡️ Paper
⚪ SphericalHarmonics
@ai_machinelearning_big_data
👍14🔥2
🧬 Genomepy: genes and genomes at your fingertips
genomepy, which can search, download, and preprocess the right genomic data for your analysis.
$ pip install genomepy
⚙️ Github
➡️ Paper
📄 Documentation
@ai_machinelearning_big_data
genomepy, which can search, download, and preprocess the right genomic data for your analysis.
$ pip install genomepy
⚙️ Github
➡️ Paper
📄 Documentation
@ai_machinelearning_big_data
👍12🔥3
🦾 XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation
⚙️ Github
➡️ Paper
💻 Tutorial page
📄 Dataset
@ai_machinelearning_big_data
⚙️ Github
➡️ Paper
💻 Tutorial page
📄 Dataset
@ai_machinelearning_big_data
👍16