​​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