Peak-End-Net: A Peak-End Rule Inspired Framework for Generalizable Video Aesthetic Assessment
Merged summary
TL;DR — Peak-End-Net is a lightweight, interpretable framework for video aesthetic assessment (VAA) that borrows the psychological "peak-end rule" to weight salient moments and endings, achieving state-of-the-art results on two benchmarks.
- Transfers knowledge from image aesthetic assessment (IAA) via a pretrained IAA head that produces frame-wise aesthetic priors, used to identify salient moments and guide peak-end-based temporal aggregation.
- Adds an aesthetic rhythm encoder to model how a video evolves aesthetically over time, plus a dynamic gated fusion mechanism for robustness under distribution shift.
- Built on a frozen ViT with only a small number of trainable parameters, making it parameter-efficient and scalable.
- Reports state-of-the-art performance on in-domain VADB and cross-domain DIVIDE-3K; code and models released on GitHub.
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Peak-End-Net: A Peak-End Rule Inspired Framework for Generalizable Video Aesthetic Assessment
TL;DR — Peak-End-Net is a lightweight, interpretable framework for video aesthetic assessment (VAA) that borrows the psychological "peak-end rule" to weight salient moments and endings, achieving state-of-the-art results on two benchmarks.
- Transfers knowledge from image aesthetic assessment (IAA) via a pretrained IAA head that produces frame-wise aesthetic priors, used to identify salient moments and guide peak-end-based temporal aggregation.
- Adds an aesthetic rhythm encoder to model how a video evolves aesthetically over time, plus a dynamic gated fusion mechanism for robustness under distribution shift.
- Built on a frozen ViT with only a small number of trainable parameters, making it parameter-efficient and scalable.
- Reports state-of-the-art performance on in-domain VADB and cross-domain DIVIDE-3K; code and models released on GitHub.