Reproduce “A Survey on Image Aesthetic Assessment” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Abbas Anwar, Saira Kanwal, Muhammad Tahir, Muhammad Saqib, Muhammad Uzair, Mohammad Khalid Imam Rahmani, Habib Ullah
This survey paper meticulously reviews contemporary techniques for automatic image aesthetic assessment, distinguishing between hand-crafted feature-based methods and modern deep learning approaches, and provides a comparative analysis of their methodologies, performance, and identified limitations within the field.
Key insights
- Hand-crafted methods rely on low-level, manually designed features and often utilize SVMs, proving effective on smaller datasets but offering a limited aesthetic scope.
- Deep learning techniques automatically learn both low-level and high-level semantic features, generally achieving higher accuracy on larger datasets but incurring increased computational costs.
- The field lacks standardized benchmark datasets, open-source algorithm implementations, and comprehensive benchmarking practices, hindering direct comparison and progress.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.