[HTML][HTML] Multimodal transformer for unaligned multimodal language sequences
Human language is often multimodal, which comprehends a mixture of natural language,
facial gestures, and acoustic behaviors. However, two major challenges in modeling such …
facial gestures, and acoustic behaviors. However, two major challenges in modeling such …
Gated attention fusion network for multimodal sentiment classification
Y Du, Y Liu, Z Peng, X Jin - Knowledge-Based Systems, 2022 - Elsevier
Sentiment classification can explore the opinions expressed by people and help them make
better decisions. With the increasing of multimodal contents on the web, such as text, image …
better decisions. With the increasing of multimodal contents on the web, such as text, image …
Mtag: Modal-temporal attention graph for unaligned human multimodal language sequences
Human communication is multimodal in nature; it is through multiple modalities such as
language, voice, and facial expressions, that opinions and emotions are expressed. Data in …
language, voice, and facial expressions, that opinions and emotions are expressed. Data in …
What makes the difference? An empirical comparison of fusion strategies for multimodal language analysis
Multimodal video sentiment analysis is a rapidly growing area. It combines verbal (ie,
linguistic) and non-verbal modalities (ie, visual, acoustic) to predict the sentiment of …
linguistic) and non-verbal modalities (ie, visual, acoustic) to predict the sentiment of …
Excavating multimodal correlation for representation learning
A majority of previous methods for multimodal representation learning ignore the rich
correlation information inherently stored in each sample, leading to a lack of robustness …
correlation information inherently stored in each sample, leading to a lack of robustness …
A quantum-like multimodal network framework for modeling interaction dynamics in multiparty conversational sentiment analysis
Sentiment analysis in conversations is an emerging yet challenging artificial intelligence (AI)
task. It aims to discover the affective states and emotional changes of speakers involved in a …
task. It aims to discover the affective states and emotional changes of speakers involved in a …
Learning from the global view: Supervised contrastive learning of multimodal representation
The development of technology enables the availability of abundant multimodal data, which
can be utilized in many representation learning tasks. However, most methods ignore the …
can be utilized in many representation learning tasks. However, most methods ignore the …
A deep learning architecture of RA-DLNet for visual sentiment analysis
A Yadav, DK Vishwakarma - Multimedia Systems, 2020 - Springer
Visual media has become one of the most potent means of conveying opinions or
sentiments on the web. Millions of photos are being uploaded by the people on famous …
sentiments on the web. Millions of photos are being uploaded by the people on famous …
Multi-channel weight-sharing autoencoder based on cascade multi-head attention for multimodal emotion recognition
Multimodal Emotion Recognition is challenging because of the heterogeneity gap among
different modalities. Due to the powerful ability of feature abstraction, Deep Neural Networks …
different modalities. Due to the powerful ability of feature abstraction, Deep Neural Networks …
Scalevlad: Improving multimodal sentiment analysis via multi-scale fusion of locally descriptors
Fusion technique is a key research topic in multimodal sentiment analysis. The recent
attention-based fusion demonstrates advances over simple operation-based fusion …
attention-based fusion demonstrates advances over simple operation-based fusion …