Peak-End-Net: A Peak-End Rule Inspired Framework for Generalizable Video Aesthetic Assessment
Video aesthetic assessment (VAA) aims to predict how aesthetically pleasing a video is, yet remains far less explored than other visual assessment tasks. Its progress is hindered not only by the scarcity of large-scale benchmarks, but also by the intrinsic subjectivity of aesthetic judgment, which is shaped by human perception. In this paper, we revisit VAA from a psychological perspective and propose \textit{Peak-End-Net}, a lightweight and interpretable framework inspired by the \textit{peak-end rule}, which suggests that people tend to judge a temporal experience mainly according to its sal
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- PossiblePossibly related (embedding) · 49%By modeling visual saliency, AI improves ratings of artistic product designs - Tech Xplore →
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
“Fuzzy title match (0.73): “Peak-End-Net: A Peak-End Rule Inspired Framework for General” ≈ “Developer-Y/cs-video-courses””
- LinkedLinked via arxiv author · 85%Yungeng Liu →
“Peak-End-Net: A Peak-End Rule Inspired Framework for Generalizable Video Aesthetic Assessment”
- LinkedLinked via arxiv author · 85%Haiwen Li →
“Peak-End-Net: A Peak-End Rule Inspired Framework for Generalizable Video Aesthetic Assessment”
- LinkedLinked via arxiv author · 85%Kerui Chen →
“Peak-End-Net: A Peak-End Rule Inspired Framework for Generalizable Video Aesthetic Assessment”
- LinkedLinked via arxiv author · 85%Jing Tang →
“Peak-End-Net: A Peak-End Rule Inspired Framework for Generalizable Video Aesthetic Assessment”
- LinkedLinked via arxiv author · 85%Lei Sun →
“Peak-End-Net: A Peak-End Rule Inspired Framework for Generalizable Video Aesthetic Assessment”