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- W2978756075 abstract "The cohesiveness of a group is an essential indicator of the emotional state, structure and success of a group of people. We study the factors that influence the perception of group-level cohesion and propose methods for estimating the human-perceived cohesion on the group cohesiveness scale. In order to identify the visual cues (attributes) for cohesion, we conducted a user survey. Image analysis is performed at a group-level via a multi-task convolutional neural network. For analyzing the contribution of facial expressions of the group members for predicting the Group Cohesion Score (GCS), a capsule network is explored. We add GCS to the Group Affect database and propose the ‘GAF-Cohesion database’. The proposed model performs well on the database and is able to achieve near human-level performance in predicting a group’s cohesion score. It is interesting to note that group cohesion as an attribute, when jointly trained for group-level emotion prediction, helps in increasing the performance for the later task. This suggests that group-level emotion and cohesion are correlated." @default.
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- W2978756075 date "2019-07-01" @default.
- W2978756075 modified "2023-09-26" @default.
- W2978756075 title "Predicting Group Cohesiveness in Images" @default.
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- W2978756075 doi "https://doi.org/10.1109/ijcnn.2019.8852184" @default.
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