Multimodal sentiment and emotion recognition in hyperbolic space

Araño, Keith April; Orsenigo, Carlotta; Soto, Mauricio; Vercellis, Carlo

doi:10.1016/j.eswa.2021.115507

Cited by 15 publications

(7 citation statements)

References 27 publications

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“…Hong et al (2023a) show that hyperbolic classification is beneficial for visual anomaly recognition tasks, such as out-of-distribution detection in image classification and segmentation tasks. Araño et al (2021) use hyperbolic layers to perform multi-modal sentiment analysis based on the audio, video, and text modalities. Ahmad & Lecue (2022) also show the effect of hyperbolic space to perform object recognition with ultra-wide field-of-view lenses.…”

Section: Sample-to-gyroplane Learningmentioning

confidence: 99%

Hyperbolic Deep Learning in Computer Vision: A Survey

Mettes,

Ghadimi Atigh,

Keller-Ressel

et al. 2024

Int J Comput Vis

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Deep representation learning is a ubiquitous part of modern computer vision. While Euclidean space has been the de facto standard manifold for learning visual representations, hyperbolic space has recently gained rapid traction for learning in computer vision. Specifically, hyperbolic learning has shown a strong potential to embed hierarchical structures, learn from limited samples, quantify uncertainty, add robustness, limit error severity, and more. In this paper, we provide a categorization and in-depth overview of current literature on hyperbolic learning for computer vision. We research both supervised and unsupervised literature and identify three main research themes in each direction. We outline how hyperbolic learning is performed in all themes and discuss the main research problems that benefit from current advances in hyperbolic learning for computer vision. Moreover, we provide a high-level intuition behind hyperbolic geometry and outline open research questions to further advance research in this direction.

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Section: Sample-to-gyroplane Learningmentioning

confidence: 99%

Hyperbolic Deep Learning in Computer Vision: A Survey

Mettes,

Ghadimi Atigh,

Keller-Ressel

et al. 2024

Int J Comput Vis

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“…Including the calculations of the forget gate, output gate, and input gate, the formula for calculating the candidate cell is as follows ( Abdar, Nikou & Gandomi, 2021 ):…”

Section: Mvcs Model Framementioning

confidence: 99%

Application of visual communication in digital animation advertising design using convolutional neural networks and big data

Fang¹,

Gong²

2023

PeerJ Computer Science

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In the age of big data, visual communication has emerged as a critical means of engaging with customers. Among multiple modes of visual communication, digital animation advertising is an exceptionally potent tool. Advertisers can create lively and compelling ads by harnessing the power of digital animation technology. This article proposes a multimodal visual communication system (MVCS) model based on multimodal video emotion analysis. This model automatically adjusts video content and playback mode according to users’ emotions and interests, achieving more personalized video communication. The MVCS model analyses videos from multiple dimensions, such as vision, sound, and text, by training on a large-scale video dataset. We employ convolutional neural networks to extract the visual features of videos, while the audio and text features are extracted and analyzed for emotions using recurrent neural networks. By integrating feature information, the MVCS model can dynamically adjust the video’s playback mode based on users’ emotions and interaction behaviours, which increases its playback volume. We conducted a satisfaction survey on 106 digitally corrected ads created using the MVCS method to evaluate our approach’s effectiveness. Results showed that 92.6% of users expressed satisfaction with the adjusted ads, indicating the MVCS model’s efficacy in enhancing digital ad design effectiveness.

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“…Arao et al [26] proposed a method for recognizing emotions and sentiments that integrated hyperbolic space in neural network models. They added a hyperbolic output layer to existing state-of-the-art models and found that it has the potential to improve the modal's prediction accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%