Benchmarks
Datasets
Greatest papers with code
Deep TEN: Texture Encoding Network
CVPR 2017
• zhanghang1989/PyTorch-Encoding
• 
The representation is orderless and therefore is particularly useful for material and texture recognition.
Deep Filter Banks for Texture Recognition and Segmentation
Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications.
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
npj Computational Materials 2019
• PV-Lab/AUTO-XRD
• 
We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data.
DATA AUGMENTATION INTERPRETABLE MACHINE LEARNING MATERIAL CLASSIFICATION MATERIAL RECOGNITION TIME SERIES CLASSIFICATION X-RAY DIFFRACTION (XRD)
Differential Viewpoints for Ground Terrain Material Recognition
• jiaxue1993/pytorch-material-classification
•
A key concept is differential angular imaging, where small angular variations in image capture enables angular-gradient features for an enhanced appearance representation that improves recognition.
Classification of Household Materials via Spectroscopy
To explore this, we collected a dataset of spectral measurements from two commercially available spectrometers during which a robotic platform interacted with 50 flat material objects, and we show that a neural network model can accurately analyze these measurements.
Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks
Our approach achieves state-of-the-art results and enables a robot to estimate the material class of household objects with ~90% accuracy when 92% of the training data are unlabeled.
Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging
• Healthcare-Robotics/spectrovision
•
Finally, we present how a robot can combine this high resolution local sensing with images from the robot's head-mounted camera to achieve accurate material classification over a scene of objects on a table.