Search Results for author:
Found 8 papers, 2 papers with code
Light-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound
In particular, segmentations of the left ventricle can be used to derive ventricular volume, ejection fraction (EF) and other relevant measurements.
A model for full local image interpretation
We describe a computational model of humans' ability to provide a detailed interpretation of components in a scene.
Parallel mesh reconstruction streams for pose estimation of interacting hands
We present a new multi-stream 3D mesh reconstruction network (MSMR-Net) for hand pose estimation from a single RGB image.
What can human minimal videos tell us about dynamic recognition models?
In human vision objects and their parts can be visually recognized from purely spatial or purely temporal information but the mechanisms integrating space and time are poorly understood.
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images
Ullman et al. 2016 show that a slight modification of the location and size of the visible region of the minimal image produces a sharp drop in human recognition accuracy.
Complex Relations in a Deep Structured Prediction Model for Fine Image Segmentation
Many deep learning architectures for semantic segmentation involve a Fully Convolutional Neural Network (FCN) followed by a Conditional Random Field (CRF) to carry out inference over an image.
A model for interpreting social interactions in local image regions
Understanding social interactions (such as 'hug' or 'fight') is a basic and important capacity of the human visual system, but a challenging and still open problem for modeling.
Structured learning and detailed interpretation of minimal object images
We model the process of human full interpretation of object images, namely the ability to identify and localize all semantic features and parts that are recognized by human observers.
