QB-Highlights: Quality-Guided Budgeted Highlight Detection in Videos with Dense Query-Relevant Moments

Highlight detection in content-dense untrimmed videos, such as livestreams, is particularly challenging due to the prevalence of semantically similar moments and the application-driven demand of fitting a target duration budget. Existing methods typically rank segments independently based on semantic relevance, struggling to discriminate fine-grained quality among similar moments and allocating the budget to redundant selections. We therefore reformulate highlight detection as jointly selecting high-quality and complementary segments under a duration budget. To study this formulation, we introduce QB-Highlights, a large-scale benchmark of 50,000 e-commerce livestream videos with densely occurring query-relevant events and subtle quality variations. QB-Highlights is organized around two complementary dimensions: Segment Qualification, which assesses fine-grained per-segment quality; and Budgeted Composition, which evaluates set-level complementarity under a duration budget. It provides MLLM-assisted pseudo labels for training, dense human annotations for testing, and an evaluation protocol jointly measuring quality and diversity. We further propose a Qualification-then-Composition Reasoning (QCR) framework that performs structured deliberative reasoning to produce a highlight set matching the target duration. Extensive experiments show that QCR outperforms strong MLLM, moment retrieval, and highlight detection baselines on both segment quality and compositional diversity.