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

• URL: http://arxiv.org/abs/2404.09474v2
• Date: Tue, 14 May 2024 13:26:43 GMT
• Title: TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals
• Authors: Alexander Vedernikov, Puneet Kumar, Haoyu Chen, Tapio Seppanen, Xiaobai Li,
• Abstract summary: 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, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form.
• Score: 58.865901821451295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration [68.25711405944239]
  Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense deformation fields in a coarse-to-fine manner. We introduce a model-driven WiNet that incrementally estimates scale-wise wavelet coefficients for the displacement/velocity field across various scales.
  arXiv  Detail & Related papers  (2024-07-18T11:51:01Z)
• Wavelet-Inspired Multiscale Graph Convolutional Recurrent Network for Traffic Forecasting [0.0]
  We propose a Graph Conal Recurrent Network (WavGCRN) which combines multiscale analysis (MSA)-based method with Deep Learning (DL)-based method. The proposed method can offer well-defined interpretability, powerful learning capability, and competitive forecasting performance on real-world traffic data sets.
  arXiv  Detail & Related papers  (2024-01-11T16:55:48Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting [8.832864937330722]
  Long-range traffic forecasting remains a challenging task due to the intricate and extensive-temporal correlations observed in traffic networks. In this paper, we propose a architecture called Graph-based Multi-ODE Neural Networks (GRAM-ODE) which is designed with multiple connective ODE-GNN modules to learn better representations. Our extensive set of experiments conducted on six real-world datasets demonstrate the superior performance of GRAM-ODE compared with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-05-30T02:10:42Z)
• MACCIF-TDNN: Multi aspect aggregation of channel and context interdependence features in TDNN-based speaker verification [5.28889161958623]
  We propose a new network architecture which aggregates the channel and context interdependence features from multi aspect based on Time Delay Neural Network (TDNN) The proposed MACCIF-TDNN architecture can outperform most of the state-of-the-art TDNN-based systems on VoxCeleb1 test sets.
  arXiv  Detail & Related papers  (2021-07-07T09:43:42Z)
• Learning Frequency-aware Dynamic Network for Efficient Super-Resolution [56.98668484450857]
  This paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures.
  arXiv  Detail & Related papers  (2021-03-15T12:54:26Z)
• Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks [110.80088437391379]
  A graph-based framework called SMART is proposed to model and keep track of the statistics of vehicle-to-temporal (V2I) communication latency across a large geographical area. We develop a graph reconstruction-based approach using a graph convolutional network integrated with a deep Q-networks algorithm. Our results show that the proposed method can significantly improve both the accuracy and efficiency for modeling and the latency performance of large vehicular networks.
  arXiv  Detail & Related papers  (2021-03-13T06:56:29Z)
• Densely Connected Recurrent Residual (Dense R2UNet) Convolutional Neural Network for Segmentation of Lung CT Images [0.342658286826597]
  We present a synthesis of Recurrent CNN, Residual Network and Dense Convolutional Network based on the U-Net model architecture. The proposed model tested on the benchmark Lung Lesion dataset showed better performance on segmentation tasks than its equivalent models.
  arXiv  Detail & Related papers  (2021-02-01T06:34:10Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.