Transformer-based multimodal feature enhancement networks for multimodal depression detection integrating video, audio and remote photoplethysmograph signals

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

MizAR 60 for Mizar 50

Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648

Proceedings of IEEE International Conference on Computer Vision

(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 3,111

CNN architectures for large-scale audio classification

Hershey, S., Chaudhuri, S., Ellis, D. P. W., Gemmeke, J. F., Jansen, A., Moore, R. C., Plakal, M., Platt, D., Saurous, R. A., Seybold, B., et al. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952132 · Cited by 2,733

Large language models in medical and healthcare fields: applications, advances, and challenges

Wang, D., & Zhang, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10921-0 · Cited by 246

A Review of Key Technologies for Emotion Analysis Using Multimodal Information

Zhu, X., Guo, C., Feng, H., Huang, Y., Feng, Y., Wang, X., & Wang, R. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10287-z · Cited by 143

A systematic survey of air quality prediction based on deep learning

Zhang, Z., Zhang, S., Chen, C., & Yuan, J. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.03.031 · Cited by 138

Artificial intelligence and neuroscience: transformative synergies in brain research and clinical applications

Onciul, R., Tataru, C.-I., Dumitru, A. V., Crivoi, C., Serban, M., Covache-Busuioc, R.-A., Radoi, M. P., & Toader, C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020550 · Cited by 113

Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting

Fang, J., Yang, L., Wen, X., Yu, H., Li, W., Adamowski, J., & Barzegar, R. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131275 · Cited by 62

Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection

Yang, M., Ngai, E. C. ‐., Hu, X., Hu, B., Liu, J., Erol Gelenbe, A., & Leung, V. C. M. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3542324 · Cited by 56

Air quality forecasting using a spatiotemporal hybrid deep learning model based on VMD–GAT–BiLSTM

Wang, X., Zhang, S., He, L., He, L., Ren, Y., Zhang, Z., Li, J., & Zhang, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-68874-x · Cited by 58

Text-guided multimodal depression detection via cross-modal feature reconstruction and decomposition

Chen, Z., Wang, D., Lou, L., Zhang, S., Zhao, X., Jiang, S., Yu, J., & Xiao, J. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102861 · Cited by 54

Multimodal emotion recognition based on a fusion of audiovisual information with temporal dynamics

Salas-Cáceres, J., Lorenzo-Navarro, J., Freire-Obregón, D., & Castrillón-Santana, M. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20227-6 · Cited by 45

Hybrid CNN-BiLSTM architecture with multiple attention mechanisms to enhance speech emotion recognition

Poorna, S. S., Menon, V., & Gopalan, S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106967 · Cited by 42

LMVD: A large-scale multimodal vlog dataset for depression detection in the wild

He, L., Chen, K., Zhao, J., Wang, Y., Pei, E., Chen, H., Jiang, J., Zhang, S., Zhang, J., Wang, Z., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103632 · Cited by 48

LMTformer: facial depression recognition with lightweight multi-scale transformer from videos

He, L., Zhao, J., Zhang, J., Jiang, J., Qi, S., Wang, Z., & Wu, D. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05908-x · Cited by 39

MLM-EOE: Automatic Depression Detection via Sentimental Annotation and Multi-Expert Ensemble

Lin, Z., Wang, Y., Zhou, Y., Du, F., & Yang, Y. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3585599 · Cited by 43

Automatic Depression Recognition With an Ensemble of Multimodal Spatio-Temporal Routing Features

Wang, Y., Lin, Z., Yang, C., Zhou, Y., & Yang, Y. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3543226 · Cited by 36

MDKAT: Multimodal Decoupling With Knowledge Aggregation and Transfer for Video Emotion Recognition

Wang, J., Wang, C., Guo, L., Zhao, S., Wang, D., Zhang, S., Zhao, X., Yu, J., Wang, Y., Yang, Y., et al. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3571534 · Cited by 38

Learning spatiotemporal dependencies using adaptive hierarchical graph convolutional neural network for air quality prediction

Hu, W. G., Zhang, Z., Zhang, S., Chen, C., Yuan, J., Yao, J., Zhao, S., & Guo, L. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.142541 · Cited by 36

Advancements in Affective Disorder Detection: Using Multimodal Physiological Signals and Neuromorphic Computing Based on SNNs

Tian, F., Zhang, L., Zhu, L., Zhao, M., Liu, J., Dong, Q., & Zhao, Q. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3420445 · Cited by 33

Design of smart home system speech emotion recognition model based on ensemble deep learning and feature fusion

Wang, M., Ma, H., Wang, Y., & Sun, X. (2024) · Applied Acoustics · doi:10.1016/j.apacoust.2024.109886 · Cited by 29

Optimizing depression detection in clinical doctor-patient interviews using a multi-instance learning framework

Zhang, X., Li, C., Chen, W., Zheng, J., & Li, F. (2025) · Scientific Reports · doi:10.1038/s41598-025-90117-w · Cited by 37

Multimodal depression detection based on an attention graph convolution and transformer

Jia, X., Chen, J., Liu, K., Wang, Q., & He, J. (2025) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2025024 · Cited by 27

LSCAformer: Long and short-term cross-attention-aware transformer for depression recognition from video sequences

He, L., Li, Z., Tiwari, P., Zhu, F., & Wu, D. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106767 · Cited by 26

DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection

Ye, J., Zhang, J., & Shan, H. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889975 · Cited by 26

MedFuseNet: fusing local and global deep feature representations with hybrid attention mechanisms for medical image segmentation

Chen, R., He, S., Xie, J., Wang, T., Xu, Y., Fang, J., Zhao, X., Zhang, S., Wang, G., Lu, H., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-89096-9 · Cited by 25

S2DA-Net: Spatial and spectral-learning double-branch aggregation network for liver tumor segmentation in CT images

Liu, H., Yang, J., Jiang, C., He, S., Fu, Y., Zhang, S., Hu, X., Fang, J., & Ji, W. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108400 · Cited by 24

Depression Scale Dictionary Decomposition Framework for Multimodal Automatic Depression Level Prediction

Niu, M., Wang, X., Gong, J., Liu, B., Tao, J., & Schuller, B. W. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3533480 · Cited by 22

Cluster search optimisation of deep neural networks for audio emotion classification

Slade, S., Li, Z., Asadi, H., Lim, C. P., Yu, Y., Zhao, D., Panesar, A., Wu, P. F., & Gao, R. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113223 · Cited by 18

SIMMA: Multimodal Automatic Depression Detection via Spatiotemporal Ensemble and Cross-Modal Alignment

Wang, Y., Lin, Z., Teng, Y., Cheng, Y., Jiang, H., & Yang, Y. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3542986 · Cited by 18

Multimodal Interpretable Depression Analysis Using Visual, Physiological, Audio and Textual Data

Kumar, P., Misra, S., Shao, Z., Zhu, B., Raman, B., & Li, X. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00518 · Cited by 24

Automatic depression prediction via cross-modal attention-based multi-modal fusion in social networks

Wang, L., Zhang, Y., Zhou, B., Cao, S., Hu, K., & Tan, Y. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109413 · Cited by 17

FedDAAM: Federated Domain Adversarial Learning With Attention Mechanism for Privacy Preserving Multimodal Depression Assessment

He, L., Yang, W., Zhao, J., Chen, H., & Jiang, D. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3609776 · Cited by 16

New benchmark dataset and fine-grained cross-modal fusion framework for Vietnamese multimodal aspect-category sentiment analysis

Nguyen, Q. H., Nguyen, M.-V. T., & Nguyen, K. V. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01558-8 · Cited by 16

Enhancing multimodal depression detection with intra- and inter-sample contrastive learning

Li, M., Wei, Y., Zhu, Y., Wei, S., & Wu, B. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121282 · Cited by 16

An historical overview of artificial intelligence for diagnosis of major depressive disorder

Liu, H., Wu, H., Yang, Z., Ren, Z., Dong, Y., Zhang, G., & Li, M. D. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1417253 · Cited by 16

Fine-grained multimodal fusion for depression assisted recognition based on hierarchical knowledge-enhanced prompt learning

Yang, S., Liu, S., Nie, G., Wang, L., Wang, T., You, J., & Cambria, E. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128532 · Cited by 17

Neural Machine Translation with CARU-Embedding Layer and CARU-Gated Attention Layer

Im, S., & Chan, K. (2024) · Mathematics · doi:10.3390/math12070997 · Cited by 14

Multi-Modal Fused-Attention Network for Depression Level Recognition Based on Enhanced Audiovisual Cues

Zhou, Y., Yu, X., Huang, Z., Palati, F., Zhao, Z., He, Z. H., Feng, Y., & Luo, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3545587 · Cited by 12

Audio-Visual Feature Disentanglement and Fusion Network for Automatic Depression Severity Prediction

Li, S., Shao, Z., Qin, R., Huang, Y., Liang, P., Li, X., Jiang, Y., Deng, Y., Liu, T., & Tan, X. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3611238 · Cited by 11

Hierarchical transformer speech depression detection model research based on Dynamic window and Attention merge

Yue, X., Zhang, C., Wang, Z., Yu, Y., Cong, S., Shen, Y., & Zhao, J. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2348 · Cited by 13

STE-Mamba: Automated Multimodal Depression Detection through Emotional Analysis and Spatio-Temporal Information Ensemble

Lin, Z., Wang, Y., Zhou, Y., Du, F., & Yang, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889512 · Cited by 11

Conformal Depression Prediction

Li, Y.-H., Qu, S.-H., & Zhou, X.-Z. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3542023 · Cited by 15

Sequence-to-point learning based on spatio-temporal attention fusion network for non-intrusive load monitoring

Zhang, S., Fu, Y., Zhao, X., Fang, J., Liu, Y., Wang, X., Zhang, B., & Yu, J. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-01803-1 · Cited by 9

VISTANet: VIsual Spoken Textual Additive Net for Interpretable Multimodal Emotion Recognition

Kumar, P., Malik, S., Raman, B., & Li, X. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3573180 · Cited by 14

Attention-Guided Bi-direction Temporal-aware Network for speech-based depression recognition

Liu, J., Shang, Y., Yang, M., Shao, Z., Ding, H., & Liu, T. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105359 · Cited by 9

Cross-modal interaction and multi-source visual fusion for video generation in fetal cardiac screening

Zhu, G., Deng, E., Qin, Z., Khan, F., Wei, W., Srivastava, G., Xiong, H., & Kumari, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102510 · Cited by 9

MDDMamba: A Model for Multi-Modal Depression Detection with a Memory-Saving Cross-Modal Attention Mechanism Based on Mamba

Liu, M., Wang, Z., & Liu, F. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822358 · Cited by 9

Nonlinear Regularization Decoding Method for Speech Recognition

Zhang, J., Wang, L., Yu, Y., & Xu, M. (2024) · Sensors · doi:10.3390/s24123846 · Cited by 8

Sample self-selection using dual teacher networks for pathological image classification with noisy labels

Han, G., Guo, W., Zhang, H., Jin, J., Gan, X., & Zhao, X. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108489 · Cited by 7

A Novel Audio-Visual Multimodal Semi-Supervised Model Based on Graph Neural Networks for Depression Detection

Li, Y., Sun, C., & Dong, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888673 · Cited by 7

Automated Speech Analysis for Screening and Monitoring Bipolar Depression: Machine Learning Model Development and Interpretation Study

Min, S., Yeum, T.-S., Shin, D., Rhee, S. J., Lee, H., Lee, H.-S., Park, S., Lee, J., & Ahn, Y. M. (2025) · JMIR Medical Informatics · doi:10.2196/79093 · Cited by 6

ParaLBench: A Large-Scale Benchmark for Computational Paralinguistics Over Acoustic Foundation Models

Zhang, Z., Xu, W., Dong, Z., Wang, K., Wu, Y., Peng, J., Wang, R., & Huang, D. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3506554 · Cited by 8

Multimodal Depression Assessment Framework Integrating Personality and Gait for Older Adults With Medical Conditions

Zhao, Y., Zhang, H., Li, J., Song, S., Lian, C., Liu, Y., Wang, Y., & Fu, C. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3552835 · Cited by 5

Counterfactual discriminative micro-expression recognition

Li, Y., Liu, M., Lao, L., Wang, Y., & Cui, Z. (2024) · Visual Intelligence · doi:10.1007/s44267-024-00063-w · Cited by 6

Multi-level spatiotemporal graph attention fusion for multimodal depression detection

Yang, Y., & Zheng, W. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108123 · Cited by 7

Attention-driven contextual feature fusion network for facial videos-based depression recognition

Xiao, H., Liu, X., Zhou, T., Qian, L., & Huang, X. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108806 · Cited by 4

Synthesis application of electronic tongue and electronic eye combined with a transformer-graph fusion network for hawthorn origin traceability

Ma, J., Sun, T., Li, X., Wang, Y., Zeng, W., Wang, Z. W., & Lan, Y. (2025) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-025-03314-6 · Cited by 3

Auxiliary diagnosis of depression in youth based on 3D-CBSResNet12 and multi-modal fusion strategy

Wang, Y., Sun, K., & Guo, Z. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103482 · Cited by 3

SquareNet: multi-scale progressive difference and scale-cross attention network for volumetric medical image segmentation

Liu, H., Sun, W., Zhang, F., Fu, Y., Jin, J., & Fang, J. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ae1103 · Cited by 3

MDD-MARF: a multimodal depression detection model based on multi-level attention mechanism and residual fusion

Zhou, J., Ge, J., Chen, Z., Tan, J., & Li, Y. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104965 · Cited by 3

Cross-Modal Fusion Mamba for Multimodal Depression Detection

Zhou, B., Fiedler, M.-A., & Al-Hamadi, A. K. (2025) · International symposium on image and signal processing and analysis/ISPA ... · doi:10.1109/ispa66905.2025.11259458 · Cited by 3

Bridging Gaps in Sundown Syndrome Research: a Scoping Review and Roadmap for Future Multimodal Approaches

Xu, Q., Lin, F. V., Liu, Y. M., & Zhao, G. (2025) · Archives of Clinical Neuropsychology · doi:10.1093/arclin/acaf062 · Cited by 4

Multimodal Time-Series Recognition With Spatio–Temporal Dynamic Graph Spike Neural Networks

Hu, B., Zeng, H., & Guan, Z. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3586037 · Cited by 3

Multimodal Local Global Interaction Networks for Automatic Depression Severity Estimation

Niu, M., Shao, Z., He, Y., Tao, J., & Schuller, B. W. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3612697 · Cited by 3

Investigating Acoustic-Textual Emotional Inconsistency Information for Automatic Depression Detection

Su, R., Xu, C., Yu, H., Wu, X., Xu, F., Chen, X., Wang, L., & Yan, N. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3651667 · Cited by 5

ERBMA-Net: Enhanced Random Binary Multilevel Attention Network for Facial Depression Recognition

Khan, M. T., Cao, Y., Faisal Shafait, B., & Jun, W. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3596047 · Cited by 2

FC-AEN: Fully Convolutional Adaptive Ensemble Network for Automatic Depression Detection

Wang, Y., Lin, Z., Teng, Y., Cheng, Y., Jiang, H., & Yang, Y. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3602264 · Cited by 2

Self-supervised learning-based EEG emotion recognition using spatial–temporal attention networks and contrastive learning

Li, J., Ma, X., Wang, T., Wang, C., Guo, L., Zhao, S., Lou, J., & Zhang, S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108800 · Cited by 2

ASND-YOLO: An improved small object detection algorithm for UAV aerial images

Huang, J., & Dong, H. (2026) · Digital Signal Processing · doi:10.1016/j.dsp.2026.106110 · Cited by 2

A Symphony of Sentiments using Log-Melspectrogram Techniques for Emotional Classification

Jain, E., Gill, K. S., Kumar, M., & Rawat, R. (2024) · International Conference on Circuit, Power and Computing Technologies · doi:10.1109/iccpct61902.2024.10672862 · Cited by 2

Multimodal sentiment analysis based on multi‐stage graph fusion networks under random missing modality conditions

Zhang, T., Song, B., Zhang, Z., & Zhang, Y. (2024) · IET Image Processing · doi:10.1049/ipr2.13310 · Cited by 2

Audio Emotion Mapping using Enhanced Techniques with Augmented Log-Melspectrograms

Jain, E., Gill, K. S., Malhotra, S., & Devliyal, S. (2024) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon62057.2024.10837795 · Cited by 1

Real-Time Feedback System for Stage Performance Using Multi-Modal Deep Learning Techniques

Qin, H., & Jiāng, L. (2025) · International Conference on Informatics Education and Computer Technology Applications (IECA) · doi:10.1145/3732801.3732860 · Cited by 1

Generalized multimodal depression severity prediction method based on topic prompts

Zhao, D., Li, Q., Na, Q., Xie, H., & Jiang, T. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103554 · Cited by 3

LiteGuard: Efficient Offline Language Model for Resource-Constrained Healthcare

Steve, T. S. T., & Yeluripati, G. R. (2025) · Conference on Information Communications Technology and Society (ICTAS) · doi:10.1109/ictas64866.2025.11155512 · Cited by 1

Classification of neurological and mental health disorders based on multimodal approaches: A comprehensive review

Dino, H. I., & Hassan, M. M. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106399 · Cited by 3

DepFormer: A Unified Framework with Bimodal Collaborative Transformer for Depression Detection

Liu, F., Zhao, S., Yin, K., Xu, T., & Chen, E. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3762062 · Cited by 1

Modeling Multimodal Depression Diagnosis From the Perspective of Local Depressive Representation

Liang, J., Cao, P., Wang, C., Yang, J., Wang, F., & Zai͏̈ane, O. R. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3625612 · Cited by 1

DSTC: A Multimodal Network for Depression Emotion Recognition and Sentiment Analysis

Lu, J., Shang, Y., Shao, Z., Liu, T., Ding, H., & Hu, Q. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3628664 · Cited by 1

FaceDepth: A Robust Unimodal Depression Detection Framework Using Invariant Facial Landmark Features

Xu, R., Chen, J., Zhang, R., Dai, G., & Mao, K. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3777463 · Cited by 1

TREAT: Facial Depression Recognition by Learning Joint Depression Score and Level Distribution

Zhang, F., Dong, L., Kim, B.-G., Wang, J., Li, K., Kumari, S., & Lv, J. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2024.9010246 · Cited by 1

Two-stage multiple instance learning networks with attention-based hybrid aggregation for speech emotion recognition

Zhang, S., Chen, C., Wang, D., Tao, X., & Zhao, X. (2026) · Computer Speech & Language · doi:10.1016/j.csl.2026.101946 · Cited by 1

Explainable visual emotion recognition via modular reasoning

Fujimoto, M. L., Marcacini, R. M., & Rezende, S. O. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113501 · Cited by 1

AttMamba: Mamba with attention mechanism for multimodal depression detection

Zheng, X., Liu, Y., Zha, Z. Y., Lv, M., & Wang, H. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133570 · Cited by 1

Large Language Models for Depression Detection: A Review with Prospects of Incomplete Multimodality

Dai, A., Shi, W., Gu, X., & Huang, L. (2026) · Brain Sciences · doi:10.3390/brainsci16060593 · Cited by 1

Harmonizing Emotions: A Novel Approach to Audio Emotion Classification using Log-Melspectrogram with Augmentation

Agarwal, M., Gill, K. S., Thapliyal, N., & Rawat, R. S. (2024) · International Conference on Communication, Computing and Internet of Things (IC3IoT) · doi:10.1109/ic3iot60841.2024.10550216 · Cited by 1

PACMR: Progressive Adaptive Crossmodal Reinforcement for Multimodal Apparent Personality Traits Analysis

Shen, P., Wang, D., Xu, Y., Zhang, S., & Zhao, X. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3505799 · Cited by 4

Contrastive Learning Joint Regularization for Pathological Image Classification with Noisy Labels

Guo, W., Han, G., Mo, Y., Zhang, H., Fang, J., & Zhao, X. (2024) · Electronics · doi:10.3390/electronics13132456

Emotional Resonance Unleashed by exploring Novel Audio Classification Techniques with Log- Melspectrogram Augmentation

Agarwal, M., Upadhyay, D., Gill, K. S., & Devliyal, S. (2024) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon60325.2024.10687403

Achieving Enhanced Bi-Linear Attention Network for Teaching Manner Analysis Over Edge Cloud-Assisted AIoT: Voice-Body Coordination Perspective

Zhou, Y., Zou, S., Wu, B., Ni, W., & Du, X. (2025) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2025.3568394

Fine-Tuning Large Language Models With Behavioral Alignment for Depression Detection

Ning, X., Sun, H., Yu, D., Yang, C., Fang, R., Fan, L., Lin, Q., & Zhu, Y. (2025) · IEEE Intelligent Systems · doi:10.1109/mis.2025.3584888

Feature-based enhanced boosting algorithm for depression detection

Rohei, M. S., Varathan, K. D., Shivakumara, P., & Anuar, N. B. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2981

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