Recently, there has been a raising surge of momentum for deep representation learning in hyperbolic spaces due to their high capacity of modeling data like knowledge graphs or synonym hierarchies, possessing hierarchical structure. We refer it as hyperbolic deep neural network in this paper. Such a hyperbolic neural architecture potentially leads to drastically compact models with much more physical interpretability than its counterpart in Euclidean space. To stimulate future research, this paper presents a coherent and comprehensive review of the literature around the neural components in the construction of hyperbolic deep neural networks, as well as the generalization of the leading deep approaches to the hyperbolic space. It also presents current applications around various machine learning tasks on several publicly available datasets, together with insightful observations and identifying open questions and promising future directions.
We introduce a new dataset for the emotional artificial intelligence research: identity-free video dataset for Micro-Gesture Understanding and Emotion analysis (iMiGUE). Different from existing public datasets, iMiGUE focuses on nonverbal body gestures without using any identity information, while the predominant researches of emotion analysis concern sensitive biometric data, like face and speech. Most importantly, iMiGUE focuses on micro-gestures, i.e., unintentional behaviors driven by inner feelings, which are different from ordinary scope of gestures from other gesture datasets which are mostly intentionally performed for illustrative purposes. Furthermore, iMiGUE is designed to evaluate the ability of models to analyze the emotional states by integrating information of recognized micro-gesture, rather than just recognizing prototypes in the sequences separately (or isolatedly). This is because the real need for emotion AI is to understand the emotional states behind gestures in a holistic way. Moreover, to counter for the challenge of imbalanced sample distribution of this dataset, an unsupervised learning method is proposed to capture latent representations from the micro-gesture sequences themselves. We systematically investigate representative methods on this dataset, and comprehensive experimental results reveal several interesting insights from the iMiGUE, e.g., micro-gesture-based analysis can promote emotion understanding. We confirm that the new iMiGUE dataset could advance studies of micro-gesture and emotion AI.
Sparse coding has achieved a great success in various image processing studies. However, there is not any benchmark to measure the sparsity of image patch/group because sparse discriminant conditions cannot keep unchanged. This paper analyzes the sparsity of group based on the strategy of the rank minimization. Firstly, an adaptive dictionary for each group is designed. Then, we prove that group-based sparse coding is equivalent to the rank minimization problem, and thus the sparse coefficient of each group are measured by estimating the singular values of each group. Based on that measurement, the weighted Schatten p-norm minimization (WSNM) has been found to be the closest solution to the real singular values of each group. Thus, WSNM can be equivalently transformed into a non-convex p -norm minimization problem in group-based sparse coding. To make the proposed scheme tractable and robust, the alternating direction method of multipliers (ADMM) is used to solve the p -norm minimization problem. Experimental results on two applications: image inpainting and image compressive sensing (CS) recovery have shown that the proposed scheme outperforms many state-ofthe-art methods.Index Terms-group sparsity, rank minimization, the weighted schatten p-norm, p -norm, adaptive dictionary.
Emotions are central for human intelligence and should have a similar role in AI. When it comes to emotion recognition, however, analysis cues for robots were mostly limited to human facial expressions and speech. As an alternative important non-verbal communicative fashion, the body gesture is proved to be capable of conveying emotional information which should gain more attention. Inspired by recent researches on micro-expressions, in this paper, we try to explore a specific group of gestures which are spontaneously and unconsciously elicited by inner feelings. These gestures are different from common gestures for facilitating communications or to express feelings on ones own initiative and always ignored in our daily life. This kind of subtle body movements is known as 'micro-gestures' (MGs). Work of interpreting the human hidden emotions via these specific gestural behaviors in unconstrained situations, however, is limited. It is because of an unclear correspondence between body movements and emotional states which need multidisciplinary efforts from computer science, psychology, and statistic researchers. To fill the gap, we built a novel Spontaneous Micro-Gesture (SMG) dataset containing 3,692 manually labeled gesture clips. The data collection from 40 participants was conducted through a story-telling game with two emotional state settings. In this paper, we explored the emotional gestures with a sign-based measurement. To verify the latent relationship between emotional states and MGs, we proposed a framework that encodes the objective gestures to a Bayesian network to infer the subjective emotional states. Our experimental results revealed that, most of the participants would do 'micro-gestures' spontaneously to relieve their mental strains. We also carried out a human test on ordinary and trained people for comparison. The performance of both our framework and human beings was evaluated on 142 testing instances (71 for each emotional state) by subject-independent testing. To authors' best knowledge, this is the first presented MG dataset. Results showed that the proposed MG recognition method achieved promising performance. We also showed that MGs could be helpful cues for the recognition of hidden emotional states.
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