Learning Visual Categories based on Probabilistic Latent Component Models with Semi-supervised Labeling

This paper proposes a learning method of object andscene categories based on probabilistic latent component modelsin conjunction with semi-supervised object class labeling. In thismethod, a set of object segments extracted from scene images ofeach scene category is firstly clustered by the probabilistic latentcomponent analysis with the variable number of classes, next theprobabilistic latent component tree is generated as a classificationtree of all the object classes of all the scene categories, andthen object classes are incrementally labeled by propagatingprior scene category labels and posterior object category labelsgiven to representative object instances of some object classes asteaching signals. Through experiments by using images of pluralcategories in an image database, it is shown that the methodworks effectively in learning a labeled object category tree andobject category composition of scene categories and achieves highperformance for object and scene recognition

Evolutionary NAS with Gene Expression Programming of Cellular Encoding

The renaissance of neural architecture search (NAS) has seen classical methods such as genetic algorithms (GA) and genetic programming (GP) being exploited for convolutional neural network (CNN) architectures. While recent work have achieved promising performance on visual perception tasks, the direct encoding scheme of both GA and GP has functional complexity deficiency and does not scale well on large architectures like CNN. To address this, we present a new generative encoding scheme -- $symbolic\ linear\ generative\ encoding$ (SLGE) -- simple, yet powerful scheme which embeds local graph transformations in chromosomes of linear fixed-length string to develop CNN architectures of variant shapes and sizes via evolutionary process of gene expression programming. In experiments, the effectiveness of SLGE is shown in discovering architectures that improve the performance of the state-of-the-art handcrafted CNN architectures on CIFAR-10 and CIFAR-100 image classification tasks; and achieves a competitive classification error rate with the existing NAS methods using less GPU resources.Comment: Accepted at IEEE SSCI 2020 (7 pages, 3 figures

Drug-Glycoprotein Interaction Analysis with Mutual Attention Neural Networks

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Stochastic Attentional Selection and Shift on the Visual Attention Pyramid

This paper proposes a computational model of visual attention which performs stochastic attentional selection and shift on the visual attention pyramid that is computed for each image frame of a video sequence. In this model, the visual attention pyramid is generated according to the rareness criteria by using intensity contrast, saturation contrast, hue contrast, orientation and motion energy on a Gaussian resolution pyramid. On this attention pyramid, stochastic attentional selection and shift is performed on mechanisms of the dynamic maintenance of IOR(Inhibition Of Return), the bottom-up spatial attention and the adaptive competitive filtering of attention. Experimental results show that this model achieves stochastic visual pop-out to artificial pop-out targets and stochastic attentional selection and shift, especially the whole-part attention shift and the motion-follow attention, in daily scenes

Stochastic Attentional Selection and Shift on the Visual Attention Pyramid

This paper proposes a computational model of visual attention which performs stochastic attentional selection and shift on the visual attention pyramid that is computed for each image frame of a video sequence. In this model, the visual attention pyramid is generated according to the rareness criteria by using intensity contrast, saturation contrast, hue contrast, orientation and motion energy on a Gaussian resolution pyramid. On this attention pyramid, stochastic attentional selection and shift is performed on mechanisms of the dynamic maintenance of IOR(Inhibition Of Return), the bottom-up spatial attention and the adaptive competitive filtering of attention. Experimental results show that this model achieves stochastic visual pop-out to artificial pop-out targets and stochastic attentional selection and shift, especially the whole-part attention shift and the motion-follow attention, in daily scenes

Simulator Complex for RoboCup Rescue Simulation Project { As Test-Bed for Multi-agent Organizational Behavior in Emergency Case of Large-Scale Disaster

Abstract. In the RoboCup Rescue Simulation Project, several kinds of simulator such as Building-Collapse and Road-Blockage Simulator, Fire Spread Simulator and Tra c Flow Simulator are expected to provide a complicated situation in the case of the large-scale disaster through their synergistic e ect. It is called Simulator Complex. This article addresses, rst, system components of the prototype version of this Simulator Complex, then, explains each of the simulators, and nally refers on the Space-Time GIS(Geographical Information System) which isexpected to play a role of DBMS(DataBase Management System) in the whole project.

An Enforcement of 'Distinct Teaching Practice' at Hiroshima University (IV)

本稿の目的は, 本年度完成した「特色ある教育実習プログラム」の実施に関して, 事前指導と本実習の接続性に焦点を当て検証することである。検証の結果, 以下の点が明らかになった。①事前指導と本実習の接続性に関して, 概ね効果的であった。事前指導における学習指導案の作成方法, 教材研究の仕方, 児童・生徒理解が本実習に生かされている。本実習に向けての心構えや意識の向上, 必要な力量への気づき等が本実習へのスムーズな導入に繋がっている。②本実習を想定した創意工夫ある事前指導が各附属学校においてなされているが, 事前指導を通して明確化された課題に対して本実習までにどのような指導をするのか, その指導における大学と附属学校の役割と連携のあり方について検討を行う必要がある。③「接続性」に関する最大の課題は, 一連の各教育実習科目の位置づけ, 目的, 及び意義について, 教育実習生, 大学及び附属学校教職員が再度認識し直し共有化することにある