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Introduction

Zero-shot learning has emerged as a promising approach for video retrieval, as it allows the model to recognize object classes without any training data. This thesis aims to explore the potential of zero-shot learning for video retrieval and evaluate its effectiveness in real-world scenarios.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of zero-shot learning
2.2 Zero-shot learning for image recognition
2.3 Zero-shot learning for video retrieval
2.4 Challenges in zero-shot learning
2.5 State-of-the-art techniques in zero-shot learning
2.6 Applications of zero-shot learning in video retrieval
2.7 Evaluation metrics for zero-shot learning
2.8 Zero-shot learning datasets
2.9 Transfer learning in zero-shot learning
2.10 Future directions in zero-shot learning

Chapter 3: Research Methodology
3.1 Data collection
3.2 Preprocessing of video data
3.3 Zero-shot learning model selection
3.4 Training process
3.5 Evaluation process
3.6 Performance metrics
3.7 Experimental setup
3.8 Parameter tuning
3.9 Validation techniques

Chapter 4: Discussion of Findings
4.1 Performance evaluation of zero-shot learning model
4.2 Comparison with state-of-the-art techniques
4.3 Analysis of results
4.4 Limitations and challenges encountered
4.5 Insights gained from the study
4.6 Implications for real-world applications
4.7 Future research directions
4.8 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contribution to the field
5.3 Recommendations for future research
5.4 Conclusion

Thesis Overview on Zero-shot Learning for Video Retrieval

Zero-shot learning for video retrieval is a cutting-edge research topic that aims to address the limitations of traditional video retrieval methods by leveraging the power of zero-shot learning. This thesis will provide a comprehensive overview of the current state of research in zero-shot learning for video retrieval, including an in-depth analysis of the challenges, techniques, and applications of this emerging technology.

Chapter 1 will introduce the topic of zero-shot learning for video retrieval, providing background information, stating the problem statement, objectives, limitations, scope, significance, and structure of the thesis. Additionally, key terms and definitions relevant to the study will be provided to enhance understanding.

Chapter 2 will present a thorough literature review on zero-shot learning, including its application in image recognition and video retrieval, challenges faced, state-of-the-art techniques, evaluation metrics, datasets, transfer learning, and future directions. This chapter will serve as a foundation for understanding the current landscape of zero-shot learning research.

Chapter 3 will delve into the research methodology employed in this study, including data collection, preprocessing, model selection, training and evaluation processes, performance metrics, experimental setup, parameter tuning, and validation techniques. This chapter will detail the steps taken to conduct the study and analyze the results.

Chapter 4 will present a detailed discussion of the findings from the study, including the performance evaluation of the zero-shot learning model, comparison with existing techniques, analysis of results, limitations, insights gained, implications for real-world applications, and future research directions. This chapter will provide a critical analysis of the research outcomes.

Chapter 5 will offer a conclusion and summary of the thesis, highlighting the key findings, contributions to the field, recommendations for future research, and concluding remarks. This chapter will tie together the main points discussed throughout the thesis and provide closure to the study on zero-shot learning for video retrieval.

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