Action Recognition Based on Dense Action Captioning
Chenglong Zhang, Lijuan Marissa Zhou, Changyong Niu · 2023
Considering that visually similar actions may be easily distinguished in textual description, it provides an opportunity to introduce textual description to assist action recognition. This paper proposes a novel action recognition method based on dense action captioning. Considering that an action may include multiple sub-actions with temporal relationship, this paper extends the thought of video captioning into actions. Multiple descriptions are generated from an action video sequence and each description corresponds to one sub-action sequence. In this paper, temporal constraints are added into a dense video captioning model for dense action captioning. With the generated descriptions, actions are recognized by a decision fusion strategy on both visual and textual presentation. The classification of visually similar actions can be refined based on textual classification of generated descriptions. The proposed method could be used in other action recognition models based on only visual representation. Experiments conducted on WorkoutUOW-18 and TAPOS datasets demonstrate the effectiveness of the proposed method on action captioning and classification.