AAAI Major Conference

AAAI Major Conference Paper
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AAAI Major Conference Paper
Banerjee, A., Bhattacharya, U., & Bera, A. (2022). Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders. Proceedings of the AAAI Conference on Artificial Intelligence, 36(1), 3–10. https://doi.org/10.1609/aaai.v36i1.19873
Research in emotion learning is crucial to many fields, such as affective computing, robotics, and human-computer interaction. According to existing research on emotion identification, an individual’s emotional state can be determined by observing their facial expressions, voice, gestures, and gait. According to psychological research, people may tell how someone feels by looking at affective cues like how fast their arms swing or how often they move. In more recent research, Bhattacharya et al. (2020a) mapped pose sequences to labeled emotions by combining such affective variables with posture dynamics retrieved using spatial-temporal graph convolutional networks. The selected reading is relevant to this discussion because it outlines emotion representation, identification from non-verbal body language, and pertinent advancements in zero-shot learning.
The demand for sizable, well-labeled datasets to construct grouping processes using earlier labeled emotions poses a significant hurdle for these machine learning-based emotion identification systems. However, it is time-consuming and frequently prohibitively expensive to generate large-scale datasets with suitable instances for each emotion, given the enormous range of human emotions and many emotion representations. Since labels for various classes are sometimes missing, zero-shot learning has received much attention as a solution. Research shows that it offers a different approach that does not rely on preexisting labels. Instead, generating the proper labels depends on the relationships between other visible and invisible classes.
In the generalized zero-shot learning (GZSL) model, a network is trained using data annotations that are only available for visible classes and then learns to distinguish all visible and invisible categories. By using data from other modalities, such as language semantics, to construct class entrenching that can correspond to each label, the model learns to generalize on the unseen classes. Recent solutions to the zero-shot problem have synthesized features for the unknown types using generative models, which are subsequently used for the classification challenge. The most popular techniques for syncing these properties are GANs and VAEs. However, research has demonstrated that VAEs’ modeling of multi-modal distributions might lead to less-than-ideal learned representations. Although GANs can produce features of more advanced quality than VAEs, their intellectual latent distribution spaces may be vulnerable to mode collapse.
The research offers a generalized zero-shot approach to identify emotions from upper-body poses created from 3D motion-captured gesture sequences. Zhan et al. (2019) previously demonstrated emotion perception from visuals in a zero-shot scenario. The researchers used the rich word entrenchments of the pre-tr...