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
👉 @bigdata_1
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
👉 @bigdata_1
⚡1
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
👉 @bigdata_1
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
👉 @bigdata_1
⚡1
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
👉 @bigdata_1
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
👉 @bigdata_1
⚡2
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
👉 @bigdata_1
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
👉 @bigdata_1
⚡2
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
👉 @bigdata_1
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
👉 @bigdata_1
👍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
👉 @bigdata_1
Github: https://github.com/alibaba/EasyCV
Paper: https://arxiv.org/abs/2208.13040v1
Datasets: https://paperswithcode.com/dataset/coco
👉 @bigdata_1
👍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/
👉 @bigdata_1
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/
👉 @bigdata_1
⚡1🤩1
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
👉 @bigdata_1
$ 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
👉 @bigdata_1
❤1⚡1
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
👉 @bigdata_1
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
👉 @bigdata_1
⚡2
Map-free Visual Relocalization: Metric Pose Relative to a Single Image
⚙️Github: https://github.com/nianticlabs/map-free-reloc
📄Paper: https://arxiv.org/abs/2210.05494v1
Project: https://research.nianticlabs.com/mapfree-reloc-benchmark
🗒Dataset: https://paperswithcode.com/dataset/kitti
👉 @bigdata_1
⚙️Github: https://github.com/nianticlabs/map-free-reloc
📄Paper: https://arxiv.org/abs/2210.05494v1
Project: https://research.nianticlabs.com/mapfree-reloc-benchmark
🗒Dataset: https://paperswithcode.com/dataset/kitti
👉 @bigdata_1
👍1
Bayesian Optimisation & Reinforcement Learning Research
pip install HEBO
Github: https://github.com/huawei-noah/HEBO
🗒 Docs: https://hebo.readthedocs.io/en/latest/
T-LBO: https://github.com/huawei-noah/HEBO/blob/master/T-LBO
Reinforcement Learning Research : https://github.com/huawei-noah/HEBO/tree/master/SAUTE
👉 @bigdata_1
pip install HEBO
Github: https://github.com/huawei-noah/HEBO
🗒 Docs: https://hebo.readthedocs.io/en/latest/
T-LBO: https://github.com/huawei-noah/HEBO/blob/master/T-LBO
Reinforcement Learning Research : https://github.com/huawei-noah/HEBO/tree/master/SAUTE
👉 @bigdata_1
⚡2
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
Github: https://github.com/pdebench/pdebench
🗒 Paper: https://arxiv.org/abs/2210.07182v1
Dataset https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2986
Pre-Trained Models : https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2987
👉 @bigdata_1
Github: https://github.com/pdebench/pdebench
🗒 Paper: https://arxiv.org/abs/2210.07182v1
Dataset https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2986
Pre-Trained Models : https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2987
👉 @bigdata_1
⚡1
Benchmarking ML for MD simulation
Molecular dynamics simulation with machine learning force fields.
Github: https://github.com/kyonofx/mdsim
🗒 Paper: https://arxiv.org/abs/2210.07237v1
Guide: https://github.com/kyonofx/mdsim#install-other-dependencies
Dataset: https://paperswithcode.com/dataset/md17
👉 @bigdata_1
Molecular dynamics simulation with machine learning force fields.
Github: https://github.com/kyonofx/mdsim
🗒 Paper: https://arxiv.org/abs/2210.07237v1
Guide: https://github.com/kyonofx/mdsim#install-other-dependencies
Dataset: https://paperswithcode.com/dataset/md17
👉 @bigdata_1
LAVIS - A Library for Language-Vision Intelligence
LAVIS is a Python deep learning library for LAnguage-and-VISion intelligence research and applications.
git clone https://github.com/salesforce/LAVIS.git
cd LAVIS
pip install .
Github: https://github.com/salesforce/lavis
🗒 Paper: https://arxiv.org/abs/2210.08773v1
Models: https://github.com/salesforce/LAVIS/tree/main/examples
Docs: https://opensource.salesforce.com/LAVIS//latest/index.html
Dataset: https://paperswithcode.com/dataset/ok-vqa
👉 @bigdata_1
LAVIS is a Python deep learning library for LAnguage-and-VISion intelligence research and applications.
git clone https://github.com/salesforce/LAVIS.git
cd LAVIS
pip install .
Github: https://github.com/salesforce/lavis
🗒 Paper: https://arxiv.org/abs/2210.08773v1
Models: https://github.com/salesforce/LAVIS/tree/main/examples
Docs: https://opensource.salesforce.com/LAVIS//latest/index.html
Dataset: https://paperswithcode.com/dataset/ok-vqa
👉 @bigdata_1
❤2
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study
conda create -n knowprompt python=3.8
conda activate knowprompt
Github: https://github.com/zjunlp/KnowPrompt
🗒 Paper: https://arxiv.org/abs/2210.10678v1
Dataset: https://github.com/zjunlp/KnowPrompt/blob/master/dataset/semeval
👉 @bigdata_1
conda create -n knowprompt python=3.8
conda activate knowprompt
Github: https://github.com/zjunlp/KnowPrompt
🗒 Paper: https://arxiv.org/abs/2210.10678v1
Dataset: https://github.com/zjunlp/KnowPrompt/blob/master/dataset/semeval
👉 @bigdata_1
❤1
MetaFormer Baselines for Vision
Github: https://github.com/sail-sg/metaformer
🗒 Paper: https://arxiv.org/abs/2210.13452v1
Dataset: https://paperswithcode.com/dataset/imagenet
👉 @bigdata_1
Github: https://github.com/sail-sg/metaformer
🗒 Paper: https://arxiv.org/abs/2210.13452v1
Dataset: https://paperswithcode.com/dataset/imagenet
👉 @bigdata_1
Перед вами — книга-метод, книга-наставник, способная перевернуть мышление и помочь выработать осознанный подход к работе и жизни в целом путём высокоуровневого планирования.
В своей второй книге IT-архитектор, методолог, педагог Александр Бындю раскрывает наиболее эффективный на сегодняшний день метод стратегического планирования, получивший название «Карта гипотез». Карта учит, как достигать намеченной цели через гипотезы, и служит навигатором на этом пути. В основу книги легли годы практики, и с уверенностью можно сказать, что Карта гипотез позволяет создать стратегию достижения для самых амбициозных целей.
У Карты гипотез нет ограничений по области применения или роли пользователя — она будет полезна в самых разных ситуациях, от личного планирования до планирования работы огромной компании. Автор надеется, что его метод, которому посвящена эта книга, послужит вашему благополучию во всех сферах жизни.
Ознакомиться с книгой
В своей второй книге IT-архитектор, методолог, педагог Александр Бындю раскрывает наиболее эффективный на сегодняшний день метод стратегического планирования, получивший название «Карта гипотез». Карта учит, как достигать намеченной цели через гипотезы, и служит навигатором на этом пути. В основу книги легли годы практики, и с уверенностью можно сказать, что Карта гипотез позволяет создать стратегию достижения для самых амбициозных целей.
У Карты гипотез нет ограничений по области применения или роли пользователя — она будет полезна в самых разных ситуациях, от личного планирования до планирования работы огромной компании. Автор надеется, что его метод, которому посвящена эта книга, послужит вашему благополучию во всех сферах жизни.
Ознакомиться с книгой
🤡1
DiffusionDB
DiffusionDB is the first large-scale text-to-image prompt dataset.
Github: https://github.com/poloclub/diffusiondb
🗒 Paper: https://arxiv.org/abs/2210.14896v1
Dataset: https://huggingface.co/datasets/poloclub/diffusiondb
👉 @bigdata_1
DiffusionDB is the first large-scale text-to-image prompt dataset.
Github: https://github.com/poloclub/diffusiondb
🗒 Paper: https://arxiv.org/abs/2210.14896v1
Dataset: https://huggingface.co/datasets/poloclub/diffusiondb
👉 @bigdata_1
❤1🔥1