Congratulations to Heming Zhang for Passing Her Qualifying Exam on Jan. 24, 2019! Her thesis proposal is titled with “LOCAL-AWARE DEEP LEARNING: METHODOLOGY AND APPLICATIONS”. Her Qualifying Exam Committee includes: Jay Kuo (Chair), Antonio Ortega, Keith Jenkins, Sandy Sawchuk and Stefanos Nikolaidis (Outside Member).

Abstract of thesis proposal:

Deep learning techniques utilize networks with multiple layers cascaded to map the inputs to desired outputs. To map the entire inputs to desired outputs, useful information should be extracted through the layers. During the mapping, feature extraction and prediction are jointly performed. We do not have direct control for feature extraction. Consequently, some useful information, especially local information, is also discarded in the process.

In this thesis proposal, we specifically study local-aware deep learning techniques
with: 1) multi-modal attention mechanism; 2) local cues reasoning; 3) local region
characteristics analysis. Specifically, we design a multi-modal attention mechanism for generative visual dialogue system, which simultaneously attends to multi-modal inputs and utilizes extracted local information to generate dialogue responses. We propose a proposal network for fast face detection system for mobile devices, which detects salient facial parts and
uses them as local cues for detection of entire faces. We extract representative fashion features by analyzing local regions, which contain local fashion details of humans’ interests.