Audio-Visual Fusion for Sentiment Classification using Cross-Modal Autoencoder

By Imran A. Sheikh, Rupayan Chakraborty, Sunil Kumar Kopparapu. 2018

Subfields: Multimodal Learning, Sentiment & Opinion Analysis

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Multimodal Learning

1. Percy Liang
2. Louis-philippe Morency
3. Ruslan Salakhutdinov
4. Yao-Hung Hubert Tsai
5. Shaojie Bai
6. J. Kolter
7. Soujanya Poria
8. Amir Zadeh

In Sentiment & Opinion Analysis

1. Percy Liang
2. Louis-philippe Morency
3. Soujanya Poria

Citations in the corpus

• Multimodal Transformer for Unaligned Multimodal Language Sequences (2019, methodology): “…representations, Wang et al. (2019) adjusted the word representations using accompanying non-verbal behaviors, Pham et al. (2019) learned robust multimodal representations using a cyclic translation objective, and Dumpala et al. (2019) explored cross-modal autoencoders for audio-visual alignment.”