# A Cross-Domain Approach for Continuous Impression Recognition from Dyadic Audio-Visual-Physio Signals

arXiv: https://arxiv.org/abs/2203.13932 · HTML: https://mlsanity.com/paper/2203.13932/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [Similarity-Preserving Knowledge Distillation](https://mlsanity.com/paper/1907.09682/) (2019)
- [Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation](https://mlsanity.com/paper/2105.08919/) (2021)
- [Fusing ASR Outputs in Joint Training for Speech Emotion Recognition](https://mlsanity.com/paper/2110.15684/) (2021)
- [Multitask Learning and Multistage Fusion for Dimensional Audiovisual Emotion Recognition](https://mlsanity.com/paper/2002.11312/) (2020)

## Abstract (arXiv)

The impression we make on others depends not only on what we say, but also, to a large extent, on how we say it. As a sub-branch of affective computing and social signal processing, impression recognition has proven critical in both human-human conversations and spoken dialogue systems. However, most research has studied impressions only from the signals expressed by the emitter, ignoring the response from the receiver. In this paper, we perform impression recognition using a proposed cross-domain architecture on the dyadic IMPRESSION dataset. This improved architecture makes use of cross-domain attention and regularization. The cross-domain attention consists of intra- and inter-attention mechanisms, which capture intra- and inter-domain relatedness, respectively. The cross-domain regularization includes knowledge distillation and similarity enhancement losses, which strengthen the feature connections between the emitter and receiver. The experimental evaluation verified the effectiveness of our approach. Our approach achieved a concordance correlation coefficient of 0.770 in competence dimension and 0.748 in warmth dimension.