Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar [arXiv] [Project] [BibTeX] Features A single architecture for panoptic, instance and semantic segmentation. Support major segmentation datasets: ADE20K, Cityscapes, COCO, Mapillary Vistas. Installation See installation instructions. Getting Started See Preparing Datasets for Mask2Former. See Getting Started with Mask2Former. Advanced usage See Advanced Usage of Mask2Former. Model Zoo and Baselines We provide a large set of baseline results and    

This app uses FastAPI as backend. Usage First install required libraries by running: pip install -r requirements.txt To run the application run following command in src dir: uvicorn main:app –reload or Tutorial main page fastapi documentation http://localhost:8000/docs show results http://localhost:8000/imshow GitHub   To finish reading, please visit source site

Teaser video with live editing sessions can be found here This code demonstrates the ideas discussed in arXiv submission Real Image Inversion via Segments.http://arxiv.org/abs/2110.06269(David Futschik, Michal Lukáč, Eli Shechtman, Daniel Sýkora) Abstract:We present a simple, yet effective approach to editingreal images via generative adversarial networks (GAN). Unlike previoustechniques, that treat all editing tasks as an operation that affects pixelvalues in the entire image in our approach we cut up the image into a set ofsmaller segments. For those segments corresponding […]

Introduction In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data. A metric learning loss functionis utilized to learn to produce pixel-wise feature embeddings such that pixels from the same object are close to each other and pixels from different objects are separated in the embedding space. With the learned feature embeddings, a mean shift clustering algorithm can be applied to discover and segment unseen objects. We further improve […]

The implementation of Reducing Infromation Bottleneck for Weakly Supervised Semantic Segmentation, Jungbeom Lee, Jooyoung Choi, Jisoo Mok, and Sungroh Yoon, NeurIPS 2021. [[paper]] Abstract Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminative region of the target object. We interpret this phenomenon using the information bottleneck principle: the final layer of a deep neural network, activated by the sigmoid or softmax activation functions, causes […]

Model segmentation classification Augmentations Used following transforms from [albumentations] RESIZE_SIZE = 1024 # or 768 train_transform = albumentations.Compose([ albumentations.Resize(RESIZE_SIZE, RESIZE_SIZE), albumentations.OneOf([ albumentations.RandomGamma(gamma_limit=(60, 120), p=0.9), albumentations.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2,    

by Bo Dong, Wenhai Wang, Deng-Ping Fan, Jinpeng Li, Huazhu Fu, & Ling Shao. This repo is the official implementation of “Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers”. 1. Introduction Polyp-PVT is initially described in arxiv. Most polyp segmentation methods use CNNs as their backbone, leading to two key issues when exchanging information between the encoder and decoder: 1) taking into account the differences in contribution between different-level features; and 2) designing effective mechanism for fusing these features. Different from […]

Overview We release the code of the DSANet (Dynamic Segment Aggregation Network). We introduce the DSA module to capture relationship among snippets for video-level representation learning. Equipped with DSA modules, the top-1 accuracy of I3D ResNet-50 is improved to 78.2% on Kinetics-400. The core code to implement the Dynamic Segment Aggregation Module is codes/models/modules_maker/DSA.py. [July 7, 2021] We release the core code of DSANet. [July 3, 2021] DSANet has been accepted by ACMMM 2021. Prerequisites All dependencies can be installed […]

The code of: A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains, arXiv pre-print 2019 paper. Introduction We conduct the first comprehensive analysis of Weakly-Supervised Semantic Segmentation (WSSS) with image label supervision in different image domains. WSSS has been almost exclusively evaluated on PASCAL VOC2012 but little work has been done on applying to different image domains, such as histopathology and satellite images. The paper analyzes the compatibility of different methods for representative datasets and presents principles for […]

CaraNet CaraNet: Context Axial Reverse Attention Network for Small Medical Objects Segmentation This repository contains the implementation of a novel attention based network (CaraNet) to segment the polyp (CVC-T, CVC-ClinicDB, CVC-ColonDB, ETIS and Kvasir) and brain tumor (BraTS). The CaraNet show great overall segmentation performance (mean dice) on polyp and brain tumor, but also show great performance on small medical objects (small polyps and brain tumors) segmentation. The technique report is here: CaraNet Architecture of CaraNet Backbone We use Res2Net […]