MaskSearch: Querying Image Masks at Scale

Machine learning tasks over image databases often generate masks that annotate image content (e.g., saliency maps, segmentation maps) and enable a variety of applications (e.g., determine if a model is learning spurious correlations or if an image was maliciously modified to mislead a model). While queries that retrieve examples based on mask properties are valuable to practitioners, existing systems do not support such queries efficiently. In this paper, we formalize the problem and propose a system, MaskSearch, that focuses on accelerating queries over databases of image masks. MaskSearch leverages a novel indexing technique and an efficient filter-verification query execution framework. Experiments on real-world datasets with our prototype show that MaskSearch, using indexes approximately 5% the size of the data, accelerates individual queries by up to two orders of magnitude and consistently outperforms existing methods on various multi-query workloads that simulate dataset exploration and analysis processes.

VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building

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.