​Semantic Image Scene Segmentation by Deep Machine Learning (27 November 2020, 3.00PM)

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27 Nov 2020 03.00 PM - 04.30 PM Public

The details of the seminar are as follows:
 Date : 27 November 2020 (Friday)
 Time : 3.00 PM – 4.30 PM
 Venue : Online via Zoom (pre-registration required at https://suss.zoom.us/meeting/register/tJIrf--qpzwqE9fLTZDf5g2LwKCRO1BZMzKW)  
Co-organized by IEEE Signal Processing Singapore Chapter, IEEE Circuits and Systems Singapore Chapter, Singapore University of Social Sciences (SUSS), together with CISS, Centre for Information Sciences and Systems, School of Electrical and Electronic Engineering, NTU

Abstract is:

Scene segmentation is a challenging task as it need classify every pixel in the image. It is crucial to exploit discriminative context and aggregate multi-scale features to achieve better segmentation. Context is essential for semantic segmentation. Due to the diverse shapes of objects and their complex layout in various scene images, the spatial scales and shapes of contexts for different objects have very large variation. It is thus ineffective or inefficient to aggregate various context information from a predefined fixed region. In this talk, I will first present a novel context contrasted local feature that not only leverages the informative context but also spotlights the local information in contrast to the context. The proposed context contrasted local feature greatly improves the parsing performance, especially for inconspicuous objects and background stuff. Furthermore, I will present a scheme of gated sum to selectively aggregate multi-scale features for each spatial position. The gates in this scheme control the information flow of different scale features. Their values are generated from the testing image by the proposed network learnt from the training data so that they are adaptive not only to the training data, but also to the specific testing image. Finally, I will present a scale- and shape-variant semantic mask for each pixel to confine its contextual region. To this end, a novel paired convolution is proposed to infer the semantic correlation of the pair and based on that to generate a shape mask. Using the inferred spatial scope of the contextual region, a shape-variant convolution is controlled by the shape mask that varies with the appearance of input. In this way, the proposed network aggregates the context information of a pixel from its semantic-correlated region instead of a predefined fixed region. In addition, this work also proposes a labeling denoising model to reduce wrong predictions caused by the noisy low-level features. This talk is based on two papers: H. Ding, X. Jiang, et al, “Context contrasted feature and gated multi-scale aggregation for scene segmentation,” CVPR’2018 Oral, and H. Ding, X. Jiang, et al, “Semantic Correlation Promoted Shape-Variant Context for Segmentation,” CVPR’2019 Oral.

Speaker's Biography:

Xudong Jiang received the B.Eng. and M.Eng. from the University of Electronic Science and Technology of China (UESTC), and the Ph.D. degree from Helmut Schmidt University, Hamburg, Germany, all in electrical engineering. From 1986 to 1993, he was a Lecturer with UESTC, where he received two Science and Technology Awards from the Ministry for Electronic Industry of China. From 1998 to 2004, he was with the Institute for Infocomm Research, A-Star, Singapore, as a Lead Scientist and the Head of the Biometrics Laboratory, where he developed a system that achieved the most efficiency and the second most accuracy at the International Fingerprint Verification Competition in 2000. He joined Nanyang Technological University (NTU), Singapore, as a Faculty Member, in 2004, and served as the Director of the Centre for Information Security from 2005 to 2011. Currently, he is a Tenured Associate Professor with the School of EEE, NTU. Dr Jiang holds 7 patents and has authored over 150 papers with 40 papers in the IEEE journals, including 11 papers in IEEE T-IP and 6 papers in IEEE T-PAMI. Two of his first authored papers have been listed as top 1% highly cited papers in the academic field of Engineering by Essential Science Indicators. He served as IFS Technical Committee Member of the IEEE Signal Processing Society from 2015 to 2017, Associate Editor for IEEE SPL for 2 terms from 2014 to 2018, Associate Editor for IEEE T-IP for 2 terms from 2016 to 2019 and the founding editorial board member for IET Biometrics form 2012 to 2019. Dr Jiang is currently a Senior Area Editor for IEEE T-IP and Editor-in-Chief for IET Biometrics. His current research interests include image processing, pattern recognition, computer vision, machine learning, and biometrics.

Hosted by A/P Lin Zhiping, School of EEE, NTU.

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