情報回路方式研究室

2019年1月9日（水）に、外国人招へい研究者として研究室に滞在されている米国Notre Dame大学の Yiyu Shi先生に、動画像中の物体検出に適する新しい機械学習手法に関する講演会をしていただきました。講演中から多くの質問がなされるなど極めて活発な議論があり、大変有益な講演会となりました。

SCNN: A General Distribution based Statistical Convolutional Neural Network with Application to Video Object Detection

Abstract: Various convolutional neural networks (CNNs) were developed recently that achieved accuracy comparable with that of human beings in computer vision tasks such as image recognition, object detection and tracking, etc. Most of these networks, however, process one single frame of image at a time, and may not fully utilize the temporal and contextual correlation typically present in multiple channels of the same image or adjacent frames from a video, thus limiting the achievable throughput. This limitation stems from the fact that existing CNNs operate on deterministic numbers. In this talk, I will present a novel statistical convolutional neural network (SCNN), which extends existing CNN architectures but operates directly on correlated distributions rather than deterministic numbers. By introducing a parameterized canonical model to model correlated data and defining corresponding operations as required for CNN training and inference, I will show that SCNN can process multiple frames of correlated images effectively, hence achieving significant speedup over existing CNN models. I will use a CNN based video object detection as an example to illustrate the usefulness of the proposed SCNN as a general network model. Experimental results show that even a non-optimized implementation of SCNN can still achieve 178% speedup over existing CNNs with slight accuracy degradation.