In this paper, we present a holistic framework, Relational. Space-Time Query (ReST), for evaluating video under- standing models via templated spatiotemporal ...

First, we propose an integrated framework, namely Relational Space-Time Query (ReST), for evaluating video understanding models via templated spatiotemporal ...

We propose an integrated framework, namely Relational Space-Time Query (ReST), for evaluating video understanding models via templated spatiotemporal queries.

We use the top-20 detection results for each frame because the query performance saturates even with more boxes. We experiment with three different object.

The key idea of Relational Space-Time Query (ReST) is to provide a unified framework for the analysis of activities, human-object interactions and eventually ...

Request PDF | On Jun 1, 2023, Xitong Yang and others published Relational Space-Time Query in Long-Form Videos | Find, read and cite all the research you ...

Jun 2, 2023 · Relational Space-Time Query in Long-Form Videos (CVPR 2023) Xitong Yang, Fu-Jen Chu, Matt Feiszli, Raghav Goyal, Lorenzo Torresani, ...

Relational Space-Time Query in Long-Form Videos Xitong Yang, Fu-Jen Chu, Matt Feiszli, Raghav Goyal, Lorenzo Torresani, Du Tran IEEE Computer Vision and ...

It involves developing methods that can recognize and understand complex activities, events, or interactions that unfold over longer durations of time. This ...

Nov 13, 2024 · Summary And Contributions: The authors release an egocentric long-form video dataset, HourVideo with the focus on video-language understanding.