Classification of Occluded Objects using Fast Recurrent Processing

Recurrent neural networks are powerful tools for handling incomplete data problems in computer vision, thanks to their significant generative capabilities. However, the computational demand for these algorithms is too high to work in real time, without specialized hardware or software solutions. In this paper, we propose a framework for augmenting recurrent processing capabilities into a feedforward network without sacrificing much from computational efficiency. We assume a mixture model and generate samples of the last hidden layer according to the class decisions of the output layer, modify the hidden layer activity using the samples, and propagate to lower layers. For visual occlusion problem, the iterative procedure emulates feedforward-feedback loop, filling-in the missing hidden layer activity with meaningful representations. The proposed algorithm is tested on a widely used dataset, and shown to achieve 2$\times$ improvement in classification accuracy for occluded objects. When compared to Restricted Boltzmann Machines, our algorithm shows superior performance for occluded object classification.Comment: arXiv admin note: text overlap with arXiv:1409.8576 by other author

Grounding semantics in robots for Visual Question Answering

In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning

Method for Detecting Anomalous States of a Control Object in Information Systems Based on the Analysis of Temporal Data and Knowledge

The problem of finding the anomalous states of the control object in the management information system under conditions of uncertainty caused by the incompleteness of knowledge about this object is considered. The method of classifying the current state of the control object in real time, allowing to identify the current anomalous state. The method uses temporal data and knowledge. Data is represented by sequences of events with timestamps. Knowledge is represented as weighted temporal rules and constraints. The method includes the following key phases: the formation of sequences of logical facts; selection of temporal rules and constraints; classification based on a comparison of rules and constraints. Logical facts are represented as predicates on event attributes and reflect the state of the control object. Logical rules define valid sequences of logical facts. Performing a classification by successive comparisons of constraints and weights of the rules makes it possible to more effectively identify the anomalous state since the comparison of the constraints reduces the subset of facts comparing to the current state. The method creates conditions for improving management efficiency in the context of incomplete information on the state of a complex object by using logical inference in knowledge bases for anomalous states of such control objects