FPS-Bench：面向高帧率视频理解的基准
FPS-Bench: A Benchmark for High Frame-Rate Video Understanding
提出 minFPS 这一可复用的度量，用来量化一个问题所需的最低帧率；做视频 VLM 或时序建模时，可用它诊断模型在时间采样上的瓶颈，并据此设计高帧率采样或时序聚合策略。
原文依据 We introduce a new metric, the minimum frames-per-second (minFPS), which measures the minimum frame-rate required to solve a given question.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论现代视频语言模型通常在降采样到低帧率（FPS）的视频上训练，而最常用的评测基准同样是针对低帧率输入设计的。为弥补这一不足，我们提出 FPS-Bench，一个大规模视频问答基准，旨在评测视觉语言模型理解高帧率视频的能力。我们引入一个新指标——最小帧率（minFPS），用于衡量解决给定问题所需的最低帧率。现有基准所需的 minFPS 均低于 1，而我们从严从多样化的视频来源中筛选出 1000 余个问题，并人工核验每个样本的 minFPS，最终得到一个平均需要以 7 FPS 观看视频才能求解的基准。我们对若干最优视觉语言模型的评测显示，它们的能力严重不足：在 FPS-Bench 多项选择任务上问答准确率仅为 30%，而人类的准确率达到 72%。
英文原文摘要 949 字符
Modern video-language models are typically trained on videos downsampled to low frames-per-second (FPS), and the most commonly used evaluation benchmarks are designed for low-FPS input as well. To address this shortcoming, we present FPS-Bench, a large video question-answering benchmark designed to evaluate VLMs' capabilities to understand video at high-frame rates. We introduce a new metric, the minimum frames-per-second (minFPS), which measures the minimum frame-rate required to solve a given question. While existing benchmarks require <1 minFPS, we rigorously curate more than 1000 questions from a diverse source of videos and manually verify minFPS for each example, leading to a benchmark that requires watching videos at on average 7 FPS to solve. Our evaluation of several state-of-the-art VLMs shows that they are severely lacking, achieving QA accuracy of 30% in the FPS-Bench multiple-choice task, while humans achieve 72% accuracy.