Blog
About

0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Dynamic Graph Modules for Modeling Higher-Order Interactions in Activity Recognition

      Preprint

      , , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Video action recognition, as a critical problem towards video understanding, has attracted increasing attention recently. To identify an action involving higher-order object interactions, we need to consider: 1) spatial relations among objects in a single frame; 2) temporal relations between different/same objects across multiple frames. However, previous approaches, e.g., 2D ConvNet + LSTM or 3D ConvNet, are either incapable of capturing relations between objects, or unable to handle streaming videos. In this paper, we propose a novel dynamic graph module to model object interactions in videos. We also devise two instantiations of our graph module: (i) visual graph, to capture visual similarity changes between objects; (ii) location graph, to capture relative location changes between objects. Distinct from previous models, the proposed graph module has the ability to process streaming videos in an aggressive manner. Combined with existing 3D action recognition ConvNets, our graph module can also boost ConvNets' performance, which demonstrates the flexibility of the module. We test our graph module on Something-Something dataset and achieve the state-of-the-art performance.

          Related collections

          Most cited references 14

          • Record: found
          • Abstract: not found
          • Book Chapter: not found

          Microsoft COCO: Common Objects in Context

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The graph neural network model.

            Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) is an element of IR(m) that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Geometric Deep Learning: Going beyond Euclidean data

                Bookmark

                Author and article information

                Journal
                13 December 2018
                Article
                1812.05637

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                10 pages, 7 figures
                cs.CV

                Computer vision & Pattern recognition

                Comments

                Comment on this article