Attentive pooling for Group Activity Recognition
- URL: http://arxiv.org/abs/2208.14847v1
- Date: Wed, 31 Aug 2022 13:26:39 GMT
- Title: Attentive pooling for Group Activity Recognition
- Authors: Ding Li, Yuan Xie, Wensheng Zhang, Yongqiang Tang and Zhizhong Zhang
- Abstract summary: In group activity recognition, hierarchical framework is widely adopted to represent the relationships between individuals and their corresponding group.
We propose a new contextual pooling scheme, named attentive pooling, which enables the weighted information transition from individual actions to group activity.
- Score: 23.241686027269928
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In group activity recognition, hierarchical framework is widely adopted to
represent the relationships between individuals and their corresponding group,
and has achieved promising performance. However, the existing methods simply
employed max/average pooling in this framework, which ignored the distinct
contributions of different individuals to the group activity recognition. In
this paper, we propose a new contextual pooling scheme, named attentive
pooling, which enables the weighted information transition from individual
actions to group activity. By utilizing the attention mechanism, the attentive
pooling is intrinsically interpretable and able to embed member context into
the existing hierarchical model. In order to verify the effectiveness of the
proposed scheme, two specific attentive pooling methods, i.e., global attentive
pooling (GAP) and hierarchical attentive pooling (HAP) are designed. GAP
rewards the individuals that are significant to group activity, while HAP
further considers the hierarchical division by introducing subgroup structure.
The experimental results on the benchmark dataset demonstrate that our proposal
is significantly superior beyond the baseline and is comparable to the
state-of-the-art methods.
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