ServiceNow Research

Semantic Segmentation

Consistency-CAM: Towards Improved Weakly Supervised Semantic Segmentation
Semantic segmentation is a popular task that has piqued the interest of many industries and research communities. However, acquiring …
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
Remote sensing and automatic earth monitoring are key to solve global-scale challenges such as disaster prevention, land use …
Weakly Supervised Underwater Fish Segmentation Using Affinity LCFCN
Estimating fish body measurements like length, width, and mass has received considerable research due to its potential in boosting …
A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images
Coronavirus Disease 2019 (COVID-19) has spread aggressively across the world causing an existential health crisis. Thus, having a …
Reinforced Active Learning for Image Segmentation
Learning-based approaches for semantic segmentation have two inherent challenges. First, acquiring pixel-wise labels is expensive and …
A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis
Visual analysis of complex fish habitats is an important step towards sustainable fisheries for human consumption and environmental …
Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation
We propose a Class-Based Styling method (CBS) that can map different styles for different object classes in real-time. CBS achieves …
Adaptive Masked Proxies for Few-Shot Segmentation
Deep learning has thrived by training on large-scale datasets. However, in robotics applications sample efficiency is critical. We …
Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation
We propose a Class-Based Styling method (CBS) that can map different styles for different object classes in real-time. CBS achieves …