Before revision:  In this work, we propose to address this issue via contrastive learning  . Concretely, we introduce a social contrastive loss that encourages the encoded motion representation to preserve sufficient information for distinguishing a positive future event from a set of negative ones.
After revision:  In this work, we propose to address this issue via contrastive learning with negative data augmentation . Concretely, we introduce a social contrastive loss that encourages the encoded motion representation to preserve sufficient information for distinguishing a positive future event from a set of negative ones.