Title: OmniSTVG: Toward Spatio-Temporal Omni-Object Video Grounding
Primary area: datasets and benchmarks
Keywords: Spatio-Temporal Video Grounding, Spatio-Temporal Omni-Object Video Grounding, Benchmark

Abstract: We introduce spatio-temporal omni-object video grounding, dubbed $\textbf{OmniSTVG}$, a new STVG task aiming to localize spatially and temporally all targets mentioned in the textual query within videos. Compared to classic STVG locating only a single target, OmniSTVG enables localization of not only an arbitrary number of text-referred targets but also their interacting counterparts in the query from the video, making it more flexible and practical in real scenarios for comprehensive understanding. In order to facilitate exploration of OmniSTVG, we propose $\textbf{BOSTVG}$, a large-scale benchmark dedicated to OmniSTVG. Specifically, BOSTVG contains 10,018 videos with 10.2M frames and covers a wide selection of 287 classes from diverse scenarios. Each sequence, paired with a free-form textual query, encompasses a varying number of targets ranging from 1 to 10. To ensure high quality, each video is manually annotated with meticulous inspection and refinement. To our best knowledge, BOSTVG, to date, is the first and the largest benchmark for OmniSTVG. To encourage future research, we present a simple yet effective approach, named $\textbf{OmniTube}$, which, drawing inspiration from Transformer-based STVG methods, is specially designed for OmniSTVG and demonstrates promising results. By releasing BOSTVG, we hope to go beyond classic STVG by locating every object appearing in the query for more comprehensive understanding, opening up a new direction for STVG. Our benchmark and code will be released.