Automatic group

Abstract: This paper proposes a database for group level emotion recognition in videos. The motivation is coming from the large number of information which the users are sharing online. This gives us the opportunity to use this perceived affect for various tasks. Most of the work in this area has been restricted to controlled environments. In this paper, we explore the group level emotion and cohesion in a real-world environment. There are several challenges involved in moving from a controlled environment to real-world scenarios such as face tracking limitations, illumination variations, occlusion and type of gatherings. As an attempt to address these challenges, we propose a ‘Video level Group AFfect (VGAF), database containing 1,004 videos downloaded from the web. The collected videos have a large variations in terms of gender, ethnicity, the type of social event, number of people, pose, etc. We have labelled our database for group level emotion and cohesion tasks and proposed a baseline based on the Inception V3 network on the database.

Abstract: During the past four years, a feature recognition based expert system for automatically performing group technology part coding from solid model data has been under development. The system has become a production quality tool, capable of quickly the geometry based portions of a part code with no human intervention. It has been tested on over 200 solid models, half of which are models of production Sandia designs. Its performance rivals that of humans performing the same task, often surpassing them in speed and uniformity. The feature recognition capability developed for part coding is being extended to support other applications, such as manufacturability analysis, automatic decomposition (for finite element meshing and machining), and assembly planning. Initial surveys of these applications indicate that the current capability will provide a strong basis for other applications and that extensions toward more global geometric reasoning and tighter coupling with solid modeler functionality will be necessary.

Abstract: This paper proposes a pipeline for automatic group-level affect analysis. A deep neural network-based approach, which leverages on the facial-expression information, scene information and a high-level facial visual attribute information is proposed. A capsule network-based architecture is used to predict the facial expression. Transfer learning is used on Inception-V3 to extract global image-based features which contain scene information. Another network is trained for inferring the facial attributes of the group members. Further, these attributes are pooled at a group-level to train a network for inferring the group-level affect. The facial attribute prediction network, although is simple yet, is effective and generates result comparable to the state-of-the-art methods. Later, model integration is performed from the three channels. The experiments show the effectiveness of the proposed techniques on three ‘in the wild’ databases: Group Affect Database, HAPPEI and UCLA-Protest database.

Abstract: In this paper we introduce the concept of a Cayley graph automatic group (CGA group or graph automatic group, for short) which generalizes the standard notion of an automatic group. Like the usual automatic groups graph automatic ones enjoy many nice properties: these group are invariant under the change of generators, they are closed under direct and free products, certain types of amalgamated products, and finite extensions. Furthermore, the Word Problem in graph automatic groups is decidable in quadratic time. However, the class of graph automatic groups is much wider then the class of automatic groups. For example, we prove that all finitely generated 2-nilpotent groups and Baumslag-Solitar groups B(1,n) are graph automatic, as well as many other metabelian groups.