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TRINITY: A Multi-Perspective Benchmark for Personal-Style Video Highlight Detection

Paper recorded by Signals 4 on 2026-08-30 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-30 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.CV · 计算机视觉 · first seen 2026-09-01

Abstract

Traditional video highlight detection relies on a narrow, event-centric definition of saliency, which often fails to generalize to unconstrained personal videos where highlights are heterogeneous and perspective-dependent. To address this, we introduce TRINITY, a multi-perspective benchmark that decomposes highlight saliency into three complementary dimensions, Event, Emotion, and Nature, within a

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#213 most recent of 237 cs.CV papers we have recorded · ↑ newer: Guardrail-Agnostic Societal Bias Evaluation in Large Vision-Language M · ↓ older: MotionSync: Non-Causal Refinement of Causal Tracker for Label-Efficien
Cite this page: TRINITY: A Multi-Perspective Benchmark for Personal-Style Video Highlight Detection: the #213 most recent of 237 cs.CV papers we have recorded (as of 2026-08-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/trinity-a-multi-perspective-benchmark-for-personal-style-video-highlight-detecti.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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