Visual-Textual Video Synopsis Generation - Techniques and Applications

Explore a comprehensive lecture on visual-textual video synopsis generation presented by Aidean Sharghi from the University of Central Florida. Delve into various aspects of video summarization, including its categories, applications in movie trailers, security, and personalization. Examine challenges in tackling subjectivity and learn about advanced techniques such as Determinantal Point Process (DPP) and Sequential-Hierarchical DPP. Discover the process of summarizing stories, experimental setups, and evaluation scenarios. Gain insights into dataset construction, annotation collection, and dense concept tagging. Investigate memory network encoding, qualitative results, and the development of query-focused video summarization datasets. Analyze generic and query-focused summarization results, exploring alternative approaches and frameworks. Examine caption generation networks, visual-language content matching, and purport networks. Conclude with a discussion on experiments, analysis, and future work in this cutting-edge field of video synopsis generation.

Intro
Motivation
Video Summarization: Categories
Application: Movie Trailer
Application: Security
Application: Personalization
Challenges
Tackling subjectivity
Video Summarization as Subset Selection
Determinantal Point Process
DPP for Video Summarization
Limitations
Sequential DPP
Sequential-Hierarchical DPP
Summarize the story
Experimental Setup
List of concepts
Evaluation Scenarios
Shortcomings
Constructing Dataset
Collecting Annotations
Dense Concept Tagging
Comparing summaries
User Summaries
Memory Network Encoding
Qualitative Results - Drink - Food
Summary Compiled the first query-focused video summarization dataset
Length of Summary
Length of the Summary
Sequential GDPP (SeqGDPP)
Train and Test Discrepancy
Large-Margin Objective Function
Updated Evaluation Metric
Generic Summarization Results
Query-Focused Summarization Results
Naïve Approach
Issues
Alternative Approach
Advantages
Framework
Caption Generation Network
Visual-Language Content Matching Network
Purport Network
Experiments
Analysis
Ablation Study
Quantitative Results - Visual Domain
Summary ... Chapter 3
Future Work