Fri, Apr 28, 2023 @ 11:30 AM - 12:30 PM
Thomas Lord Department of Computer Science
PhD Thesis Defense - Cho-Ying Wu
Committee Members: Ulrich Neumann (chair), Laurent Itti, Andrew Nealen, C.C. Jay Kuo
Title: Meta Learning for Single Image Depth Prediction
Abstract: Predicting geometry from images is a fundamental and popular task in computer vision and has multiple applications. For example, predicting ranges from ego view images can help robots navigate through indoor spaces and avoid collisions. Additional to physical applications, one can synthesize novel views from single images with the help of depth by warping pixels to different camera positions. Further, one can fuse depth estimation from multiple views and create a complete 3D environment for AR VR uses.
In the dissertation, we aim to discover a better learning strategy, meta learning, to learn a higher level representation. The learned representation more accurately characterizes the depth domain. Our presented meta learning approach attains better performance without involving extra data or pretrained models but directly focuses on learning schedules. Then, we closely evaluate the generalizability on our collected Campus Data and demonstrate meta learning\'s ability in sub, single, multi dataset levels.
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://usc.zoom.us/j/9340884176