Visual Saliency Prediction - Models and Evaluation: Analyzing models and evaluation metrics for predicting visual saliency, i.e., identifying the most relevant regions in images or videos
Keywords:
Visual Saliency Prediction, ModelsAbstract
Visual saliency prediction plays a crucial role in various computer vision applications by identifying the most relevant regions in images or videos. This paper presents a comprehensive review of models and evaluation metrics for visual saliency prediction. We discuss the evolution of saliency prediction models, from early bottom-up approaches to the latest deep learning-based methods. Additionally, we analyze the various evaluation metrics used to assess the performance of these models. Through a comparative study, we highlight the strengths and weaknesses of different models and metrics, providing insights into the current state-of-the-art in visual saliency prediction. Finally, we discuss future research directions and challenges in this field.
Downloads
References
Prabhod, Kummaragunta Joel. "ANALYZING THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNIQUES IN IMPROVING PRODUCTION SYSTEMS." Science, Technology and Development 10.7 (2021): 698-707.
Sadhu, Amith Kumar Reddy, and Ashok Kumar Reddy Sadhu. "Fortifying the Frontier: A Critical Examination of Best Practices, Emerging Trends, and Access Management Paradigms in Securing the Expanding Internet of Things (IoT) Network." Journal of Science & Technology 1.1 (2020): 171-195.
Tatineni, Sumanth, and Karthik Allam. "Implementing AI-Enhanced Continuous Testing in DevOps Pipelines: Strategies for Automated Test Generation, Execution, and Analysis." Blockchain Technology and Distributed Systems 2.1 (2022): 46-81.
Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.
Perumalsamy, Jegatheeswari, Chandrashekar Althati, and Lavanya Shanmugam. "Advanced AI and Machine Learning Techniques for Predictive Analytics in Annuity Products: Enhancing Risk Assessment and Pricing Accuracy." Journal of Artificial Intelligence Research 2.2 (2022): 51-82.
Devan, Munivel, Lavanya Shanmugam, and Chandrashekar Althati. "Overcoming Data Migration Challenges to Cloud Using AI and Machine Learning: Techniques, Tools, and Best Practices." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 1-39.
Althati, Chandrashekar, Bhavani Krothapalli, and Bhargav Kumar Konidena. "Machine Learning Solutions for Data Migration to Cloud: Addressing Complexity, Security, and Performance." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 38-79.
Sadhu, Ashok Kumar Reddy, and Amith Kumar Reddy. "A Comparative Analysis of Lightweight Cryptographic Protocols for Enhanced Communication Security in Resource-Constrained Internet of Things (IoT) Environments." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 121-142.
Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.