# TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals

arXiv: https://arxiv.org/abs/2404.09474 · HTML: https://mlsanity.com/paper/2404.09474/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [DAiSEE: Towards User Engagement Recognition in the Wild](https://mlsanity.com/paper/1609.01885/) (2016)
- [Improving state-of-the-art in Detecting Student Engagement with Resnet and TCN Hybrid Network](https://mlsanity.com/paper/2104.10122/) (2021)
- [Do I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselines](https://mlsanity.com/paper/2302.00431/) (2023)
- [Affect-driven Ordinal Engagement Measurement from Video](https://mlsanity.com/paper/2106.10882/) (2021)
- [Analyzing Participants' Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features](https://mlsanity.com/paper/2404.04394/) (2024)
- [Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey](https://mlsanity.com/paper/2403.08824/) (2024)
- [Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories](https://mlsanity.com/paper/1810.11392/) (2018)

## Built on by (most cited first, 7 of 7)

- [PriorNet: Prior-Guided Engagement Estimation from Face Video](https://mlsanity.com/paper/2605.03615/) (2026)
- [VisioPhysioENet: Visual Physiological Engagement Detection Network](https://mlsanity.com/paper/2409.16126/) (2024)
- [OPEN: A Benchmark Dataset and Baseline for Older Adult Patient Engagement Recognition in Virtual Rehabilitation Learning Environments](https://mlsanity.com/paper/2507.17959/) (2025)
- [DS-HGCN: A Dual-Stream Hypergraph Convolutional Network for Predicting Student Engagement via Social Contagion](https://mlsanity.com/paper/2512.20059/) (2025)
- [Safeguarding Privacy: Privacy-Preserving Detection of Mind Wandering and Disengagement Using Federated Learning in Online Education](https://mlsanity.com/paper/2602.09904/) (2026)
- [Variational Encoder--Multi-Decoder (VE-MD) for Privacy-by-functional-design (Group) Emotion Recognition](https://mlsanity.com/paper/2604.02397/) (2026)
- [Not Every Subject Should Stay: Machine Unlearning for Noisy Engagement Recognition](https://mlsanity.com/paper/2605.04713/) (2026)

## Abstract (arXiv)

Engagement analysis finds various applications in healthcare, education, advertisement, services. Deep Neural Networks, used for analysis, possess complex architecture and need large amounts of input data, computational power, inference time. These constraints challenge embedding systems into devices for real-time use. To address these limitations, we present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain and boost processing speed, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form. Evaluated on the EngageNet dataset, the proposed method outperforms existing baselines, utilizing only two behavioral features (head pose rotations) compared to the 98 used in baseline models. Furthermore, comparative analysis shows TCCT-Net's architecture offers an order-of-magnitude improvement in inference speed compared to state-of-the-art image-based Recurrent Neural Network (RNN) methods. The code will be released at https://github.com/vedernikovphoto/TCCT_Net.