Social Fabric: Tubelet Compositions for Video Relation Detection [article]

Shuo Chen, Zenglin Shi, Pascal Mettes, Cees G. M. Snoek
<span title="2021-08-18">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper strives to classify and detect the relationship between object tubelets appearing within a video as a triplet. Where existing works treat object proposals or tubelets as single entities and model their relations a posteriori, we propose to classify and detect predicates for pairs of object tubelets a priori. We also propose Social Fabric: an encoding that represents a pair of object tubelets as a composition of interaction primitives. These primitives are learned over all relations,
more &raquo; ... esulting in a compact representation able to localize and classify relations from the pool of co-occurring object tubelets across all timespans in a video. The encoding enables our two-stage network. In the first stage, we train Social Fabric to suggest proposals that are likely interacting. We use the Social Fabric in the second stage to simultaneously fine-tune and predict predicate labels for the tubelets. Experiments demonstrate the benefit of early video relation modeling, our encoding and the two-stage architecture, leading to a new state-of-the-art on two benchmarks. We also show how the encoding enables query-by-primitive-example to search for spatio-temporal video relations. Code: https://github.com/shanshuo/Social-Fabric.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.08363v1">arXiv:2108.08363v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/okjgeetdr5adrnut4hrrwcadki">fatcat:okjgeetdr5adrnut4hrrwcadki</a> </span>
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