Survey of Deep Learning Based Multimodal Emotion Recognition

Multimodal emotion recognition aims to recognize human emotional states through different modalities related to human emotion expression such as audio, vision, text, etc. This topic is of great importance in the fields of human-computer interaction, a.pngicial intelligence, affective computing, etc., and has attracted much attention. In view of the great success of deep learning methods developed in recent years in various tasks, a variety of deep neural networks have been used to learn high-level emotional feature representations for multimodal emotion recog-nition. In order to systematically summarize the research advance of deep learning methods in the field of multi-modal emotion recognition, this paper aims to present comprehensive analysis and summarization on recent multi-modal emotion recognition literatures based on deep learning. First, the general framework of multimodal emotion recognition is given, and the commonly used multimodal emotional dataset is introduced. Then, the principle of representative deep learning techniques and its advance in recent years are briefly reviewed. Subsequently, this paper focuses on the advance of two key steps in multimodal emotion recognition: emotional feature extraction methods related to audio, vision, text, etc., including hand-crafted feature extraction and deep feature extraction; multi-modal information fusion strategies integrating different modalities. Finally, the challenges and opportunities in this field are analyzed, and the future development direction is pointed out

Architecture and key technologies of coalmine underground vision computing

It has always been a common demand to stay away from the harsh environment with narrow space, numerous devices, complex operation process, and hidden hazards, and realize intelligent unmanned mining in the coal industry. To achieve this goal, it is very necessary for us to develop an effective theory of vision computing for underground coalmine applications. Its main task is to build effective models or frameworks for perceiving, describing, recognizing and understanding the environment of underground coalmine, and let intelligent equipment get 3D environment information in coalmine from images or videos. To effectively develop this theory and make it better for intelligent development of coalmine, this paper first analyzed the similarities and differences about computer vision and visual computing in coalmine, and proposed its composition architecture. And then, this paper introduced in detail the key technologies involved in visual computing in coalmine including visual perception and light field computing, feature extraction and feature description, semantic learning and vision understanding, 3D vision reconstruction, and sense computing integration and edge intelligence, which is followed by typical application cases of visual computing in coalmines. Finally, the development trend and prospect of underground visual computing in coalmine was given. In this section, this paper focused on concluding the key challenges and introducing two valuable applications including coalmine Augmented Reality/Mixed Reality and parallel intelligent mining. With the breakthrough of underground vision computing, it will play a more and more important role in the intelligent development of coal mines

A complementary view for better understanding the term computational thinking

The term Computational Thinking is closely related to efforts connected to teach a systematic and well-structured way of problem solving that includes a set of tools and techniques used in Computer Science. While substantial research in this field has shown promising outcomes concerning distinct intervention programs and teaching initiatives, the term Computational Thinking itself requires to be revised in order to get a wider consensus about its meaning and purpose. This paper contributes to the ongoing quest concerning the definition of the term by starting with a fundamental perspective on computational theory and corresponding concepts in order to describe the theoretical building blocks of a systematic view to further elaborate on an approach for teaching and learning about Computational Thinking. Additionally, based on this foundational effort, more advanced concepts are presented and discussed in order to better understand this domain. Finally, the paper identifies and discusses a set of relevant challenges taking a cognitive psychology perspective on Computational Thinking