We introduce VOCALExplore, a system designed to support users in building domain-specific models over video datasets. VOCALExplore supports interactive labeling sessions and trains models using user-supplied labels. VOCALExplore maximizes model quality by automatically deciding how to select samples based on observed skew in the collected labels. It also selects the optimal video representations to use when training models by casting feature selection as a rising bandit problem. Finally, VOCALExplore implements optimizations to achieve low latency without sacrificing model performance. We demonstrate that VOCALExplore achieves close to the best possible model quality given candidate acquisition functions and feature extractors, and it does so with low visible latency (~1 second per iteration) and no expensive preprocessing.

翻译：我们提出了VOCALExplore系统，旨在支持用户基于视频数据集构建领域专用模型。该系统支持交互式标注会话，并利用用户提供的标签训练模型。VOCALExplore通过根据收集标签中的观察偏斜自动决定样本选择策略，最大程度提升模型质量；同时将特征选择建模为上升强盗问题，以选择训练模型时的最优视频表示。此外，VOCALExplore实现了在不牺牲模型性能前提下的低延迟优化。实验表明，在给定候选采集函数和特征提取器的情况下，VOCALExplore能够以接近最优的模型质量实现推理，且具备低可见延迟（约每次迭代1秒）和无需昂贵预处理的特点。