# CMIS-Net: A Cascaded Multi-Scale Individual Standardization Network for Backchannel Agreement Estimation

arXiv: https://arxiv.org/abs/2510.17855 · HTML: https://mlsanity.com/paper/2510.17855/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 51 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Implicit Semantic Data Augmentation for Deep Networks](https://mlsanity.com/paper/1909.12220/) (2019)
- [On Feature Normalization and Data Augmentation](https://mlsanity.com/paper/2002.11102/) (2020)
- [A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion](https://mlsanity.com/paper/1507.02221/) (2015)
- [Massive Exploration of Neural Machine Translation Architectures](https://mlsanity.com/paper/1703.03906/) (2017)
- [A Light Weight Model for Active Speaker Detection](https://mlsanity.com/paper/2303.04439/) (2023)
- [Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour](https://mlsanity.com/paper/1801.06055/) (2018)
- [LEED: Label-Free Expression Editing via Disentanglement](https://mlsanity.com/paper/2007.08971/) (2020)
- [Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor](https://mlsanity.com/paper/1706.01340/) (2017)
- [MultiMediate '22: Backchannel Detection and Agreement Estimation in Group Interactions](https://mlsanity.com/paper/2209.09578/) (2022)
- [Backchannel Detection and Agreement Estimation from Video with Transformer Networks](https://mlsanity.com/paper/2306.01656/) (2023)
- [One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification](https://mlsanity.com/paper/1909.03082/) (2019)
- [DSNet: Disentangled Siamese Network with Neutral Calibration for Speech Emotion Recognition](https://mlsanity.com/paper/2312.15593/) (2023)

## Abstract (arXiv)

Backchannels are subtle listener responses, such as nods, smiles, or short verbal cues like "yes" or "uh-huh," which convey understanding and agreement in conversations. These signals provide feedback to speakers, improve the smoothness of interaction, and play a crucial role in developing human-like, responsive AI systems. However, the expression of backchannel behaviors is often significantly influenced by individual differences, operating across multiple scales: from instant dynamics such as response intensity (frame-level) to temporal patterns such as frequency and rhythm preferences (sequence-level). This presents a complex pattern recognition problem that contemporary emotion recognition methods have yet to fully address. Particularly, existing individualized methods in emotion recognition often operate at a single scale, overlooking the complementary nature of multi-scale behavioral cues. To address these challenges, we propose a novel Cascaded Multi-Scale Individual Standardization Network (CMIS-Net) that extracts individual-normalized backchannel features by removing person-specific neutral baselines from observed expressions. Operating at both frame and sequence levels, this normalization allows model to focus on relative changes from each person's baseline rather than absolute expression values. Furthermore, we introduce an implicit data augmentation module to address the observed training data distributional bias, improving model generalization. Comprehensive experiments and visualizations demonstrate that CMIS-Net effectively handles individual differences and data imbalance, achieving state-of-the-art performance in backchannel agreement detection.