In this paper, the task of text segmentation is approached from a topic modeling perspective. We investigate the use of latent Dirichlet allocation (LDA) topic model to segment a ...
Given a set of images of scenes containing multiple object categories (e.g. grass, roads, buildings) our objective is to discover these objects in each image in an unsupervised man...
This paper uses Factored Latent Analysis (FLA) to learn a factorized, segmental representation for observations of tracked objects over time. Factored Latent Analysis is latent cl...
Extractive multi-document summarization is the task of choosing sentences from a set of documents to compose a summary text in response to a user query. We propose a generative ap...
In this paper, we review two techniques for topic discovery in collections of text documents (Latent Semantic Indexing and K-Means clustering) and present how we integrated them in...