Automatic Group Cohesiveness Detection With Multi-modal Features

@article{Zhu2019AutomaticGC,
  title={Automatic Group Cohesiveness Detection With Multi-modal Features},
  author={Bin Zhu and Xin Guo and Kenneth E. Barner and Charles G. Boncelet},
  journal={2019 International Conference on Multimodal Interaction},
  year={2019}
}
Group cohesiveness is a compelling and often studied composition in group dynamics and group performance. The enormous number of web images of groups of people can be used to develop an effective method to detect group cohesiveness. This paper introduces an automatic group cohesiveness prediction method for the 7th Emotion Recognition in the Wild (EmotiW 2019) Grand Challenge in the category of Group-based Cohesion Prediction. The task is to predict the cohesive level for a group of people in… Expand
Automatic Prediction of Group Cohesiveness in Images
This paper discusses the prediction of cohesiveness of a group of people in images. The cohesiveness of a group is an essential indicator of the emotional state, structure and success of the group.Expand
D2C-Based Hybrid Network for Predicting Group Cohesion Scores
TLDR
This study proposed an automatic GCS estimation system for the 7th Emotion Recognition in the Wild (EmotiW 2019) challenge in the task of the Group Cohesion Prediction, and developed a joint training method called Discrete labels to Continuous scores (D2C), where discrete labels directly participate in generating continuous scores. Expand
Efficient Group-Based Cohesion Prediction in Images Using Facial Descriptors
TLDR
Experimental study on the Group Affect Dataset from EmotiW 2019 challenge demonstrated that the proposed approach allows to achieve an improvement of quality and even to reduce the running time of an algorithm’s work when compared to known solutions. Expand
LDNN: Linguistic Knowledge Injectable Deep Neural Network for Group Cohesiveness Understanding
TLDR
A linguistic knowledge injectable deep neural network (LDNN) that builds a visual model (visual LDNN) for predicting group cohesiveness that can automatically associate the linguistic knowledge hidden behind images with the visual images they see is proposed. Expand
Implicit Knowledge Injectable Cross Attention Audiovisual Model for Group Emotion Recognition
TLDR
An end-to-end architecture called implicit knowledge injectable cross attention audiovisual deep neural network (K-injection audioviisual network) that imitates this intuition of humans to understand emotions is proposed. Expand
Non-Volume Preserving-based Fusion to Group-Level Emotion Recognition on Crowd Videos
TLDR
This work extends the earlier ER investigations, which focused on either group-level ER on single images or within a video, by fully investigating grouplevel expression recognition on crowd videos by proposing an effective deep feature level fusion mechanism to model the spatial-temporal information in the crowd videos. Expand

References

SHOWING 1-10 OF 34 REFERENCES
Predicting Group Cohesiveness in Images
TLDR
It is interesting to note that group cohesion as an attribute, when jointly trained for group- level emotion prediction, helps in increasing the performance for the later task, suggesting that group-level emotion and cohesion are correlated. Expand
Group-level emotion recognition using deep models on image scene, faces, and skeletons
TLDR
A hybrid network that incorporates global scene features, skeleton features of the group, and local facial features is developed and achieves outperforming the baseline of 52.97% and 53.62% on the validation and testing sets. Expand
Understanding images of groups of people
In many social settings, images of groups of people are captured. The structure of this group provides meaningful context for reasoning about individuals in the group, and about the structure of theExpand
Group-Level Emotion Recognition Using Hybrid Deep Models Based on Faces, Scenes, Skeletons and Visual Attentions
TLDR
This paper presents a hybrid deep learning network submitted to the 6th Emotion Recognition in the Wild (EmotiW 2018) Grand Challenge, in the category of group-level emotion recognition, and achieves the first place in the challenge. Expand
Unearthed: The Other Side of Group Cohesiveness
Abstract One of the most consistently studied constructs in group dynamics research is Cohesiveness. It is a fact, that there is a tendency to see the effects of group cohesiveness as being largelyExpand
From individual to group-level emotion recognition: EmotiW 5.0
TLDR
The fifth Emotion Recognition in the Wild challenge 2017 aims at providing a common benchmarking platform for researchers working on different aspects of affective computing, and the particular focus of the challenge is to evaluate method in `in the wild' settings. Expand
Smile Detection in the Wild Based on Transfer Learning
  • Xin Guo, L. Polanía, K. Barner
  • Computer Science
  • 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)
  • 2018
TLDR
An efficient transfer learning-based smile detection approach to leverage the large amount of labeled data from face recognition datasets and to alleviate overfitting on smile detection is proposed. Expand
Recognize complex events from static images by fusing deep channels
TLDR
Inspired by the recent success of deep learning, a multi-layer framework is formulated to tackle the problem of event recognition, which takes into account both visual appearance and the interactions among humans and objects and combines them via semantic fusion. Expand
Emotion Recognition in Context
TLDR
The importance of considering the context for recognizing peoples emotions in images, and the EMCO dataset, a dataset of images containing people in context in non-controlled environments, is presented to provide a benchmark in the task of emotion recognition in visual context. Expand
EmotiW 2019: Automatic Emotion, Engagement and Cohesion Prediction Tasks
TLDR
The EmotiW benchmarking platform provides researchers with an opportunity to evaluate their methods on affect labelled data and the databases used, the experimental protocols and the baselines are discussed. Expand
...
1
2
3
4
...