Introducing a simple one-stage approach for automated text-to-image generation that achieves state-of-the-art performance on #LocalizedNarratives using only image-text pairs — no segmentation or bounding box data needed. Learn how it’s done below: https://t.co/SPMbJaVvAN
— Google AI (@GoogleAI) May 26, 2021
High-Frequency aware Perceptual Image Enhancement
— AK (@ak92501) May 26, 2021
pdf: https://t.co/1XZkAYnzrT
abs: https://t.co/sevT4gbmwu pic.twitter.com/zumPyikSzP
Intriguing Properties of Vision Transformers
— AK (@ak92501) May 24, 2021
pdf: https://t.co/mMujDE3PQF
abs: https://t.co/3crBQpq9Qd
advantages of ViTs over CNNs for occlusion handling, robustness to distributional shifts and patch permutations, automatic segmentation with pixel supervision pic.twitter.com/3u4X0rl033
This is a summary of easily available datasets for generalized DALLE-pytorch training. https://t.co/yFP083YnBt #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience #pytorch
— PyTorch Best Practices (@PyTorchPractice) May 23, 2021
Check out Know Your Data, a tool built by the PAIR team at Google to help understand datasets, improve data quality and mitigate bias issues. Try it for yourself on 71 datasets in the @TensorFlow Datasets catalog. https://t.co/b2mnaSovzk pic.twitter.com/DnRPt0TBsL
— Google AI (@GoogleAI) May 21, 2021
Excited to share the open source of EfficientNetV2 code and modes.
— Mingxing Tan (@tanmingxing) May 14, 2021
Paper: https://t.co/YHWEb8pHmR(accepted to ICML'21)
Code: https://t.co/97gKzgYtf2
Bonus: amazing speed on both TPUs and V100/A100 GPUs with TensorRT. https://t.co/rPDTusJWzW
When Does Contrastive Visual Representation Learning Work?
— AK (@ak92501) May 13, 2021
pdf: https://t.co/pw83mXu7Sn
abs: https://t.co/tSHW6h8hVs
benefit of additional pretraining data beyond
500k images is modest, adding pretraining images from
another domain does not lead to more general representations pic.twitter.com/XVHBNvI1bv
🤗 Transformers meets VISION 📸🖼️
— Hugging Face (@huggingface) May 12, 2021
v4.6.0 is the first CV dedicated release!
- CLIP @OpenAI, Image-Text similarity or Zero-Shot Image classification
- ViT @GoogleAI, and
- DeiT @facebookai, SOTA Image Classification
Try ViT/DeiT on the hub (Mobile too!):https://t.co/swIGmI6G0d pic.twitter.com/nOs8PEfmzo
Self-Supervised Learning with Swin Transformers
— AK (@ak92501) May 11, 2021
pdf: https://t.co/uNAcu1JViD
abs: https://t.co/J70bvOq6nc
github: https://t.co/5dktODVxa9
a self-supervised learning approach called MoBY, with Vision Transformers as its backbone architecture pic.twitter.com/4yJm1AWHkh
Contrastive Learning for Unsupervised Image-to-Image Translation
— AK (@ak92501) May 10, 2021
pdf: https://t.co/tiRPgcY0ic
abs: https://t.co/OoyrZ6ERqQ
learn a discriminator that differentiates between distinctive styles and let the discriminator supervise a generator to transfer those styles across images pic.twitter.com/PigLyp2795
ResMLP: Feedforward networks for image classification with data-efficient training
— AK (@ak92501) May 10, 2021
pdf: https://t.co/0cTkeTwwne
abs: https://t.co/DA1zkiYq0S
an architecture built entirely upon multi-layer perceptrons for image classification pic.twitter.com/7VmEyovIkW
CoCon: Cooperative-Contrastive Learning
— AK (@ak92501) May 3, 2021
pdf: https://t.co/BxFs21DHel
abs: https://t.co/y5vwjgu3QY
github: https://t.co/n0xYs7ZksZ
a cooperative version of contrastive learning, called CoCon, for self-supervised video representation learning pic.twitter.com/35QDE7XGrN
