ServiceNow Research

Computer Vision

A Planning based Neural-Symbolic Approach for Embodied Instruction Following
The ALFRED environment features embodied instruction following tasks in simulated home environments. However, end-to-end deep learning …
Multi-label Iterated Learning for Image Classification with Label Ambiguity
Transfer learning from large-scale pre-trained models has become essential for many computer vision tasks. Recent studies have shown …
Neural Point Light Fields
We introduce Neural Point Light Fields that represent scenes implicitly with a light field living on a sparse point cloud. Combining …
Object-centric Compositional Imagination for Visual Abstract Reasoning
Like humans devoid of imagination, current machine learning systems lack the ability to adapt to new, unexpected situations by …
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to …
A Survey of Self-Supervised and Few-Shot Object Detection
Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which …
A Deep Learning Localization Method for Measuring Abdominal Muscle Dimensions in Ultrasound Images
Health professionals extensively use Two-Dimensional (2D) Ultrasound (US) videos and images to visualize and measure internal organs …
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Explainability for machine learning models has gained considerable attention within the research community given the importance of …
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 …