Visual dialog is challenging since it needs to answer a series of coherent questions based on understanding the visual environment. How to ground related visual objects is one of the key problems. Previous studies utilize the question and history to
attend to the image and achieve satisfactory performance, while these methods are not sufficient to locate related visual objects without any guidance. The inappropriate grounding of visual objects prohibits the performance of visual dialog models. In this paper, we propose a novel approach to Learn to Ground visual objects for visual dialog, which employs a novel visual objects grounding mechanism where both prior and posterior distributions over visual objects are used to facilitate visual objects grounding. Specifically, a posterior distribution over visual objects is inferred from both context (history and questions) and answers, and it ensures the appropriate grounding of visual objects during the training process. Meanwhile, a prior distribution, which is inferred from context only, is used to approximate the posterior distribution so that appropriate visual objects can be grounding even without answers during the inference process. Experimental results on the VisDial v0.9 and v1.0 datasets demonstrate that our approach improves the previous strong models in both generative and discriminative settings by a significant margin.
This paper provides algebraic representation of Petri Nets model, taking
advantage that the releasing principle of Petri Nets depends on reduction process
of Monoiad of the commutative natural numbers.
(In conclusion) Finally, we provided a theore
m that explains how to make use of
algebraic properties, to simulate Petri Nets algebraically and identify the results
we will get after releasing a series of Petri Nets' transitions.
In our research we offer detailed study of one of the data
mining functions within the text data using the object properties in
databases. It studies the possibility of applying this function on the
Arabic texts. We use procedural query language P
L / SQL that
deals with the object of Oracle databases.
Data mining model Has been built. It works on classification
of Arabic texts documents using SVM algorithm for indexing of
texts and texts preparation, Naïve Bayes algorithm to classify data
after transformation it into nested tables. So we made an evaluation
of the obtained results and conclusions.
This paper introduces a new algorithm to solve some problems
that data clustering algorithms such as K-Means suffer from.
This new algorithm by itself is able to cluster data without the
need of other clustering algorithms.
