Deep Learning forDense Per-Pixel Prediction and Vision-to-Language Problems
Dense per-pixel prediction provides an estimate for each pixel given an image,offering much richer information
than conventional sparse prediction models. Thus the Computer Vision community have been increasingly shifting the research focus to per-pixel prediction.
In the first part of my talk, I will introduce my recent work on deep structured methods for per-pixel prediction that combine
deep learning and graphical models such as conditional random fields. I show how to improve depth estimation from single images and semantic segmentation with the use of contextual information in the context of deep structured learning.
In deep learning, the trend towards increasingly deep neural networks has been driven by a general observation that increasing depth increases the performance of a neural network. Recently, however, evidence has been amassing that simply increasing depth may not be the best way to increase performance, particularly given other limitations.As a result, a new, shallower, architecture of residual networks is proposed, which significantly outperforms much deeper models on classification as well as semantic segmentation by a large margin.
Recent advances in computer vision and natural language processing (NLP) have led to new interesting applications.
Two popular ones are automatically generating natural captions for images/video and answering questions relevant to a given image
(i.e., visual question answering or VQA).
In the second part of my talk, I will describe several recent work from my group that take advantage of state-of-the-art computer visionand NLP
techniques to produce promising results on both tasks of image captioning and VQA.
沈春华教授于2011年入职澳大利亚阿德莱德大学计算机科学学院，先后担任高级讲师、副教授、教授。目前同时担任澳大利亚机器人视觉中心(Australian Centre for Robotic Vision)的项目主任(Project Leader)、阿德莱德大学机器学习实验室主任。
他之前在澳大利亚国家信息通讯技术研究院(National ICT Australia)堪培拉实验室和澳大利亚国立大学工作近6年。沈春华教授本科毕业于南京大学强化部(现匡亚明学院)；硕士毕业于南京大学电子系，并从阿德莱德大学获得计算机视觉方向的博士学位。2012到2016年被澳大利亚研究理事会(Australian Research Council)授予Future Fellowship。