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Full-Text Articles in Computer Sciences
Learning Generalized Video Memory For Automatic Video Captioning, Poo-Hee Chang, Ah-Hwee Tan
Learning Generalized Video Memory For Automatic Video Captioning, Poo-Hee Chang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Recent video captioning methods have made great progress by deep learning approaches with convolutional neural networks (CNN) and recurrent neural networks (RNN). While there are techniques that use memory networks for sentence decoding, few work has leveraged on the memory component to learn and generalize the temporal structure in video. In this paper, we propose a new method, namely Generalized Video Memory (GVM), utilizing a memory model for enhancing video description generation. Based on a class of self-organizing neural networks, GVM’s model is able to learn new video features incrementally. The learned generalized memory is further exploited to decode the …
Deep Code Comment Generation, Xing Hu, Ge Li, Xin Xia, David Lo, Zhi Jin
Deep Code Comment Generation, Xing Hu, Ge Li, Xin Xia, David Lo, Zhi Jin
Research Collection School Of Computing and Information Systems
During software maintenance, code comments help developerscomprehend programs and reduce additional time spent on readingand navigating source code. Unfortunately, these comments areoften mismatched, missing or outdated in the software projects.Developers have to infer the functionality from the source code.This paper proposes a new approach named DeepCom to automatically generate code comments for Java methods. The generatedcomments aim to help developers understand the functionalityof Java methods. DeepCom applies Natural Language Processing(NLP) techniques to learn from a large code corpus and generatescomments from learned features. We use a deep neural networkthat analyzes structural information of Java methods for bettercomments generation. We conduct …
Quantitative Behavior Tracking Of Xenopus Laevis Tadpoles For Neurobiology Research, Alexander Hansen Hamme
Quantitative Behavior Tracking Of Xenopus Laevis Tadpoles For Neurobiology Research, Alexander Hansen Hamme
Senior Projects Fall 2018
Xenopus laevis tadpoles are a useful animal model for neurobiology research because they provide a means to study the development of the brain in a species that is both physiologically well-understood and logistically easy to maintain in the laboratory. For behavioral studies, however, their individual and social swimming patterns represent a largely untapped trove of data, due to the lack of a computational tool that can accurately track multiple tadpoles at once in video feeds. This paper presents a system that was developed to accomplish this task, which can reliably track up to six tadpoles in a controlled environment, thereby …