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Filtering by: Creator Zheng, Lei Remove constraint Creator: Zheng, Lei Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Committee Member Fern, Xiaoli Z. Remove constraint Committee Member: Fern, Xiaoli Z.

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Pixel- and Frame-level Video Labeling using Spatial and Temporal Convolutional Networks

Creator:

Lei, Peng

Abstract:

This dissertation addresses the problem of video labeling at both the frame and pixel levels using deep learning. For pixel-level video labeling, we have studied two problems: i) Spatiotemporal video segmentation and ii) Boundary detection and boundary flow estimation. For the problem of spatiotemporal video segmentation, we have developed recurrent...

Resource Type:

Dissertation

Full Text:

AN ABSTRACT OF THE DISSERTATION OF Peng Lei for the degree
Multiple Instance Multiple Label Learning with Limited Supervision

Creator:

Nguyen, Tam

Abstract:

In weak supervision learning, label information can be provided at different levels of granularity. For example, in multi-instance multi-label learning, samples are organized into bags and labels for each class are provided at the bag level. For small datasets, this approach offers means of reducing the labeling efforts. However, in...

Resource Type:

Dissertation
Two Sides of a Coin: Adversarial-Based Image Privacy and Defending Against Adversarial Perturbations for Robust CNNs

Creator:

Rajabi, Arezoo

Abstract:

Emergence of highly accurate Convolutional Neural Networks (CNNs) with the capability to process large datasets, has led to their popularity in many applications, including safety/security-sensitive (e.g. disease recognition, self-driving cars). Despite the high accuracy of convolutional neural networks, they have been found to be susceptible to adversarial noise added to...

Resource Type:

Dissertation
Convergence Analysis Framework for Fixed-Point Algorithms in Machine Learning and Signal Processing

Creator:

Vu, Trung

Abstract:

Iterative algorithms are simple yet efficient in solving large-scale optimization problems in practice. With a surge in the amount of data in past decades, these methods have become increasingly important in many application areas including matrix/tensor recovery, deep learning, data mining, and reinforcement learning. To optimize or improve iterative algorithms,...

Resource Type:

Dissertation

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Academic Affiliation

Electrical Engineering and Computer Science[remove]4

Advisor

Raich, Raviv2
Bobba, Rakesh B.1
Todorovic, Sinisa1

Commencement Year

Commencement Year range begin – Commencement Year range end

Current results range from 2019 to 2023

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Committee Member

Fern, Xiaoli Z.[remove]4
Kim, Jinsub3
Bolte, John1
Campbell, Matthew1
Li, Fuxin1
more Committee Members »

Creator

Lei, Peng1
Nguyen, Tam1
Rajabi, Arezoo1
Vu, Trung1

Contributor

Raich, Raviv1

Date

Date range begin – Date range end

Current results range from 2018 to 2022

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Decade

2020-20293
2010-20191

Degree Field

Computer Science4

Degree Level

Doctoral4

Degree Name

Doctor of Philosophy (Ph.D.)4

Language

English [eng]4

License

All rights reserved4

Peer Reviewed

No4

Resource Type

Dissertation4

Rights Statement

In Copyright4

Subject

Computer vision1
Machine learning1

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