PhD Dissertation Defense - Xuefeng Hu

PhD Dissertation Defense - Xuefeng Hu  
 
Committee members: Ram Nevatia (chair), Aram Galstyan and Keith Jenkins  
 
Title: Adapt Pre-trained Representation Towards Downstream Tasks  
 
Abstract: In recent years, the field of computer vision and machine learning has witnessed a paradigm shift, characterized by a dramatic increase in the scale of model parameters and training data sizes. This evolution has led to significant enhancements in model accuracy and robustness, transcending the traditional, task-specific expert models. The field has now pivoted towards universal, large-scale pre-trained visual representations, which enables impressive zero-shot and few-shot solutions for a wide array of downstream tasks.    
 
Despite these advancements, the application of pre-trained models to specific downstream tasks, each with their unique conditions and domain-specific challenges, often exposes inherent limitations. This dissertation aims to tackle these challenges. The research journey comprises a spectrum of approaches from fully-supervised to source-free and test-time adaptation, with diverse applications such as image classification, object detection, and forensic detection. This dissertation introduces novel architectures such as SPAN, which has pioneered the utilization of the self-attention mechanism in the field of computer vision, as well as innovative adaptation algorithms like ReCLIP and BaFTA, which enhance zero-shot classification performance with unsupervised vision-text alignment. This dissertation marks a transition from classic visual representations, like those used in ImageNet, to cutting-edge vision-language models like CLIP, and has overcome some of the most pressing challenges in the field.    
 
The works of this dissertation play an important role in bridging the gap between generic visual representations and the specific, nuanced requirements of various real-world tasks. By doing so, it establishes new benchmarks in optimizing the performance of machine learning models in practical applications, reinforcing the role of advanced computational techniques in solving complex, real-world problems.
 
Zoom Link: https://usc.zoom.us/j/95935934090?pwd=RTFNcUorbndkaXA2UGtFWWkrbEtsUT09 
Meeting ID: 959 3593 4090
Passcode: 442518
 

Location: Henry Salvatori Computer Science Center (SAL) - 213

Audiences: Everyone Is Invited

Contact: CS Events

Event Link: https://usc.zoom.us/j/95935934090?pwd=RTFNcUorbndkaXA2UGtFWWkrbEtsUT09