Semantic2Graph: Graph-based Multi-modal Feature Fusion for Action Segmentation in Videos

Video action segmentation have been widely applied in many fields. Most previous studies employed video-based vision models for this purpose. However, they often rely on a large receptive field, LSTM or Transformer methods to capture long-term dependencies within videos, leading to significant computational resource requirements. To address this challenge, graph-based model was proposed. However, previous graph-based models are less accurate. Hence, this study introduces a graph-structured approach named Semantic2Graph, to model long-term dependencies in videos, thereby reducing computational costs and …

challenge computational cs.cv cs.mm dependencies feature fields fusion graph graph-based long-term lstm modal multi-modal requirements segmentation studies transformer video videos vision vision models