Paper «A Probabilistic U-Net for Segmentation of Ambiguous Images» from #NIPS2018 spotlight presentation.

Github: https://github.com/SimonKohl/probabilistic_unet
Github: Arxiv: https://arxiv.org/abs/1806.05034

#DeepMind #segmentation #cv

Faster R-CNN and Mask R-CNN in #PyTorch 1.0

Another release from #Facebook.

Mask R-CNN Benchmark: a fast and modular implementation for Faster R-CNN and Mask R-CNN written entirely in @PyTorch 1.0. It brings up to 30% speedup compared to mmdetection during training.

Webcam demo and ipynb file are available.

Github: https://github.com/facebookresearch/maskrcnn-benchmark

#CNN #CV #segmentation #detection

​​Papers from #DeepMind panel at #NIPS2018

Work on radiotherapy planning: https://arxiv.org/abs/1809.04430
Triaging eye diseases: https://www.nature.com/articles/s41591-018-0107-6
Probabilistic U-net: https://arxiv.org/abs/1806.05034

#segmentation #CV #Unet

​​Fast video object segmentation with Spatio-Temporal GANs

Spatio-Temporal GANs to the Video Object Segmentation task, allowing to run at 32 FPS without fine-tuning.

#FaSTGAN #GAN #Segmentation #videomining #CV #DL

​​Deep Learning Image Segmentation for Ecommerce Catalogue Visual Search

Microsoft’s article on image segmentation

Link: https://www.microsoft.com/developerblog/2018/04/18/deep-learning-image-segmentation-for-ecommerce-catalogue-visual-search/

#CV #DL #Segmentation #Microsoft

​​GSCNN: video segmetation architecture

Semantic segmentation GSCNN significantly outperforms DeepLabV3+ on Cityscapes benchmark.

Paper: https://arxiv.org/abs/1907.05740
Github (Project): https://github.com/nv-tlabs/GSCNN

#DL #CV #NVidiaAI #Nvidia #autonomous #selfdriving #car #RL #segmentation

​​New paper on training with pseudo-labels for semantic segmentation

Semi-Supervised Segmentation of Salt Bodies in Seismic Images:
SOTA (1st place) at TGS Salt Identification Challenge.

Github: https://github.com/ybabakhin/kaggle_salt_bes_phalanx
ArXiV: https://arxiv.org/abs/1904.04445

#GCPR2019 #Segmentation #CV

Video on how Facebook continues to develop its #Portal device

How #Facebook used Mask R-CNN, #PyTorch, and custom hardware integrations like foveated processing to improve Portal’s Smart Camera system.

Link: https://ai.facebook.com/blog/smart-camera-portal-advances/

#CV #DL #Segmentation

YOLACT: Real-time Instance Segmentation

Fully-convolutional model for real-time instance segmentation that achieves 29.8 mAP on MS COCO at 33.5 fps evaluated on a single Titan Xp, which is significantly faster than any previous competitive approach. They obtain this result after training on only one GPU.


video: https://www.youtube.com/watch?v=0pMfmo8qfpQ
paper: https://arxiv.org/abs/1904.02689
code: https://github.com/dbolya/yolact

#yolo #instance_segmentation #segmentation #real_time

BodyPix: Real-time Person Segmentation in the Browser with TensorFlow.js

Released BodyPix 2.0 with #multiperson support and improved accuracy (based on ResNet50), a new API, weight quantization, and support for different image sizes with TensorFlow.js

It estimates and renders person and body-part segmentation at 25 fps on a 2018 15-inch MacBook Pro, and 21 fps on an iPhone X.

code: https://github.com/tensorflow/tfjs-models/tree/master/body-pix
demo: https://storage.googleapis.com/tfjs-models/demos/body-pix/index.html
blog: https://blog.tensorflow.org/2019/11/updated-bodypix-2.html

#dl #segmentation

​​SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation

New approach for interpreting medical image segmentation models.

U-Net and other image segmentation models work quite well on medical data, but still aren't widely adopted. One of the reasons is the lack of reproducibility as well as robustness issues.
The key idea of the paper is using the additional stream in U-Net with shape features to increase robustness and use the output of this stream (attention map) that can be used or interpretability.

Modifications to the basic U-Net architecture:
- use dense blocks from DenseNet-121 as the encoder.
- use dual attention decoder block (with spatial and channel-wise attention paths)
- make the second stream using object shape (contour)
- dual-task loss function: cross-entropy + dice + edge loss (bce loss of the predicted shape boundaries)

Shape and spatial attention maps can be used for interpretation.

Paper: https://arxiv.org/abs/2001.07645
Code: https://github.com/sunjesse/shape-attentive-unet


#unet #imagesegmentation #interpretability #segmentation

​​Gated Path Selection Network for Semantic Segmentation

A new approach for improving APSS-like networks for image segmentation.
Atrous Spatial Pyramid Pooling (ASPP) is an architecture that concatenates multiple atrous-convolved features using different dilution rates.

In this paper, authors develop a novel architecture named GPSNet, which aims to densely capture semantic context and to learn adaptive receptive fields, which are flexible to model various geometric deformations.

They designed architecture with multiple branches called SuperNet. The main characteristics are the following:
- it stacks a series of bottlenecked branches which consist of differently tuned dilation convolutions;
- multiple dense connections;
- a new module - Gate Prediction, which produces soft masks;
- improved sampling.

This approach was tested on Cityscapes и ADE20K datasets and showed better quality than other ASPP architectures, but still not as good as the current SOTA.
An ablation study shows that all changes introduced in this paper improve the score.
GPS module is lightweight and can be easily used in other models with ASPP architecture.


paper: https://deepai.org/publication/gated-path-selection-network-for-semantic-segmentation

#cv #semantic #segmentation #ASPP

Albumentation – fast & flexible image augmentations

Image Augmentations is a powerful technique to improve model robustness and performance. There are many image augmentations libraries on the market: torchvision, imgaug, DALI, Augmentor, SOLT, etc.
In all of them, authors focussed on variety at the cost of speed, or the speed at the cost of flexibility.

Requirements for augmentations:
* Variety: they want to have a large set of standard and exotic augmentation for image classification, segmentation, and detection in one place.
* Performance: transforms should be as fast as possible.
* Flexibility: it should be easy to add new transforms or new types of transforms.
* Conciseness: all complexity of implementation should be hidden behind the API.

Albumentations were born out of necessity. The authors were actively participating in various Deep Learning competitions. To get to the top they needed something better than what was already available. All of them, independently, started working on more powerful augmentation pipelines. Later they merged their efforts and released the code in the form of the library.

To date Albumentations has more than 70 transforms and supports image classification, #segmentation, object and keypoint detection tasks.

The library was adopted by academics, Kaggle, and other communities.

ODS: #tool_albumentations
Link: https://albumentations.ai/
Github: https://github.com/albumentations-team/albumentations
Paper: https://www.mdpi.com/2078-2489/11/2/125

P.S. Following trend setup by #Catalyst team, we provide extensive description of project with the help of its creators.

#guestpost #augmentation #CV #DL #imageprocessing #ods #objectdetection #imageclassification #tool

Image Segmentation: tips and tricks from 39 Kaggle competitions

this article gave you some background into #image #segmentation tips and tricks
also, collect some tools and frameworks that you can use to start competing

the author overview:
* architectures
* training tricks
* losses
* pre-processing
* post processing
* ensembling
* tools and frameworks

link here

​​Automatic product tagging on photoes on Facebook Pages

#FacebookAI released an improvement aiming at enhancing shopping platform.

Post: https://ai.facebook.com/blog/powered-by-ai-advancing-product-understanding-and-building-new-shopping-experiences
Paper: https://scontent-arn2-1.xx.fbcdn.net/v/t39.8562-6/99353320_565175057533429_3886205100842024960_n.pdf

#GrokNet #DL #segmentation #PyTorch