Current state-of-the-art methods for image segmentation form a dense image
representation where the color, shape and texture information are all processed
together inside a deep CNN. This however may not be ideal as they contain very
Quality-diversity (QD) algorithms search for a set of good solutions which
cover a space as defined by behavior metrics. This simultaneous focus on
quality and diversity with explicit metrics sets QD algorithms apart from
standard single- and...
Data augmentation is a critical component of training deep learning models.
Although data augmentation has been shown to significantly improve image
classification, its potential has not been thoroughly investigated for object
detection. Given the...
Humans can only interact with part of the surrounding environment due to
biological restrictions. Therefore, we learn to reason the spatial
relationships across a series of observations to piece together the surrounding
environment. Inspired by such...
We propose Text2Scene, a model that interprets input natural language
descriptions in order to generate various forms of compositional scene
representations; from abstract cartoon-like scenes to synthetic images. Unlike
recent works, our method does...