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Events for April 28, 2023
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BME Seminar Speaker, Dr. Jianping Fu
Fri, Apr 28, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Jianpin Fu , Professor, Mechanical Engineering, Biomedical Engineering, Cell & Developmental Biology, University of Michigan
Talk Title: Stem cell and developmental bioengineering
Host: BME Professor Keyue Shen - Zoom Available Upon Request
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Michele Medina
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PhD Thesis Defense - Cho-Ying Wu
Fri, Apr 28, 2023 @ 11:30 AM - 12:30 PM
Thomas Lord Department of Computer Science
University Calendar
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
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PhD Thesis Defense - Gozde Sahin
Fri, Apr 28, 2023 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense - Gozde Sahin
Title: Towards More Occlusion-Robust Deep Visual Object Tracking
Committee Members: Prof. Laurent Itti (chair), Prof. Ulrich Neumann, Prof. Keith Jenkins
Abstract: Visual object tracking (VOT) is considered as one of the principal challenges in computer vision, where a target given in the first frame is tracked in the rest of the video. Major challenges in VOT include factors such as rotations, deformations, illumination changes, and occlusions. With the widespread use of deep learning models with strong representative power, trackers have evolved to better handle the changes in the targets appearance due to factors like rotations and deformations. Meanwhile, robustness to occlusions has not been as widely studied for deep trackers and occlusion representation in VOT datasets has stayed low over the years.
In this work, we focus on occlusions in deep visual object tracking and examine whether realistic occlusion data and annotations can help with development and evaluation of more occlusion-robust trackers. First, we propose a multi-task occlusion learning framework to show how much occlusion labels in current datasets can help improve tracker performance in occluded frames. We discover that lack of representation in VOT datasets creates a barrier for developing and evaluating trackers that focus on occlusions. To address occlusions in visual tracking more directly, we create a large video benchmark for visual object tracking: The Heavy Occlusions in Object Tracking (HOOT) Benchmark. HOOT is specifically tailored for evaluation, analysis and development of occlusion-robust trackers with its extensive occlusion annotations. Finally, using the annotations in HOOT, we examine the effect of occlusions on template update and propose an occlusion-aware template update framework that improves the tracker performance under heavy occlusions.
Location: Hedco Neurosciences Building (HNB) - 100
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
