Hierarchical Transfer of Semantic Attributes

Domain Transfer Learning for Object and Action Recognition

Title of dissertation: Domain Transfer Learning for Object and Action Recognition Jingjing Zheng, Doctor of Philosophy, 2015 Dissertation directed by: Professor Rama Chellappa Department of Electrical and Computer Engineering Visual recognition has always been a fundamental problem in computer vision. Its task is to learn visual categories using labeled training data and then identify unlabeled...

Asking Friendly Strangers: Non-Semantic Attribute Transfer

Attributes can be used to recognize unseen objects from a textual description. Their learning is oftentimes accomplished with a large amount of annotations, e.g. around 160k-180k, but what happens if for a given attribute, we do not have many annotations? The standard approach would be to perform transfer learning, where we use source models trained on other attributes, to learn a separate targ...

Bridging the semantic gap for software effort estimation by hierarchical feature selection techniques

Software project management is one of the significant activates in the software development process. Software Development Effort Estimation (SDEE) is a challenging task in the software project management. SDEE is an old activity in computer industry from 1940s and has been reviewed several times. A SDEE model is appropriate if it provides the accuracy and confidence simultaneously before softwa...

A procedure for Web Service Selection Using WS-Policy Semantic Matching

In general, Policy-based approaches play an important role in the management of web services, for instance, in the choice of semantic web service and quality of services (QoS) in particular. The present research work illustrates a procedure for the web service selection among functionality similar web services based on WS-Policy semantic matching. In this study, the procedure of WS-Policy publi...

The Effect of Semantic Transfer on Iranian EFL Learners’ Lexical Representation and Processing