To this end, we consider multi-person pose estimation as a joint-to-person association problem.
#6 best model for Multi-Person Pose Estimation on MPII Multi-Person
Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.
In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution.
Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery.
In this paper, we present a novel approach, STWalk, for learning trajectory representations of nodes in temporal graphs.
In this work we perform outlier detection using ensembles of neural networks obtained by variational approximation of the posterior in a Bayesian neural network setting.
We propose a language-agnostic way of automatically generating sets of semantically similar clusters of entities along with sets of "outlier" elements, which may then be used to perform an intrinsic evaluation of word embeddings in the outlier detection task.