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Recognising human-object interactions using attention-based LSTMs.

Almushyti, Muna and Li, Frederick W. B. (2019) 'Recognising human-object interactions using attention-based LSTMs.', in Computer Graphics and Visual Computing (CGVC). , pp. 135-139.


Recognising Human-object interactions (HOIs) in videos is a challenge task especially when a human can interact with multiple objects. This paper attempts to solve the problem of HOIs by proposing a hierarchical framework that analyzes human-object interactions from a video sequence. The framework consists of LSTMs that firstly capture both human motion and temporal object information independently, followed by fusing these information through a bilinear layer to aggregate human-object features, which are then fed to a global deep LSTM to learn high-level information of HOIs. The proposed approach applies an attention mechanism to LSTMs in order to focus on important parts of human and object temporal information.

Item Type:Book chapter
Full text:Publisher-imposed embargo
(AM) Accepted Manuscript
File format - PDF
Publisher Web site:
Date accepted:22 July 2019
Date deposited:30 October 2019
Date of first online publication:12 September 2019
Date first made open access:No date available

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