Publications – Centre for Perceptual and Interactive Intelligence (CPII)

PUBLICATIONS

The following publications are sorted by the English surnames of our Principal Investigators

Prof. Rosanna Yuen-Yan CHAN

R. Y. -Y. Chan, C. M. V. Wong and Y. N. Yum, “Predicting Behavior Change in Students With Special Education Needs Using Multimodal Learning Analytics,” In IEEE Access, vol. 11, pp. 63238-63251, 2023.

C. M. V. Wong, R. Y.Y. Chan, Y. N. Yum, K.Wang. “Internet of Things (IoT)-Enhanced Applied Behavior Analysis (ABA) for Special Education Needs,” Sensors 2021, 21(19), 6693, 16 pages. 2021.

Prof. Shih-Chi CHEN

F. Han, S. Gu, A. Klimas, N. Zhao, Y. Zhao, and S. Chen, “3D Nanofabrication via Ultrafast Laser Patterning and Kinetically-regulated Material Assembly,” Science, Vol. 378, No. 6626, pp. 1325-1331, 2022.

W. Ouyang, X. Xu, W. Lu, N. Zhao, F. Han, and S. Chen,“Ultrafast 3D Nanofabrication via Digital Holography,” Nature Communications, Vol. 14, pp. 1716, 2023.

C. Li and S. Chen, “Design of Compliant Mechanisms based on Compliant Building Elements. Part I: Principles,” Precision Engineering, Vol. 81, pp. 207-220, 2023.

C. Li and S. Chen, “Design of Compliant Mechanisms based on Compliant Building Elements. Part II: Practice,” Precision Engineering, Vol. 81, pp. 8-21, 2023.

X. Li, W. Liu, F. Goudail, and S. Chen, “Optimal Nonlinear Stokes-Mueller Polarimetry for Multi-photon Processes,” Optics Letters, Vol. 47, No. 13, pp. 3287-3290, 2022.

X. Li, J. Xu, L. Zhang, H. Hu, and S. Chen, “Underwater Image Restoration via Stokes Decomposition,” Optics Letters, Vol. 47, No. 11, pp. 2854-2857, 2022.

D. Chen, S. Gu, and S. C. Chen, “Study of Optical Modulation based on Binary Masks with Finite Pixels,” Opt. Lasers Eng., vol. 142, no. 106604, 2021.

S. Yang, F. Li, M.M. Gong, L. Zhang, Z.W. Zhu, H.B. Shen, S.C Chen. “Generation of Q-switched and Mode-locked Pulses based on PbS/CdS Saturable Absorber in an Er-doped Fiber Laser,” Journal of Material Chemistry C, 10: 5956-5961. 2022.

X. Li, W. Lu, X. Xu, Y. Wang, S.C. Chen. “Advanced Optical Methods and Materials for Fabricating 3D Tissue Scaffolds,” Light: Advanced Manufacturing, 3: 26. 2022.

X. Li, F. Goudail, S.C. Chen. “Self-calibration for Mueller Polarimeters based on DoFP Polarization Imagers,” Optics Letters, 47(6): 1415-1418. 2022.

X. Liu, X. Li, S.C. Chen. “Enhanced Polarization Demosaicking Network via a Precise Angle of Polarization Loss Calculation Method,” Optics Letters, 47(5): 1065-1068. 2021.

M. Ren, W. Lu, Q. Shao, F. Han, W. Ouyang, T. Zhang, C. C.L. Wang, and S.C. Chen. “Aberration-free Large-area Stitch-free 3D Nano-printing based on Binary Holography,” Optics Express, 29(26): 44250-263. 2021.

Prof. Hongsheng LI

B. Zhu, Z. Wang, S. Shi, H. Xu, L. Hong, and H. Li, “ConQueR: Query Contrast Voxel-DETR for 3D Object Detection,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

X. Shi, Z. Huang, D. Li, M. Zhang, K. C. Cheung, S. See, H. Qin, J. Dai, and H. Li, “FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

R. Zhang, X. Hu, B. Li, S. Huang, H. Deng, H. Li, Y. Qiao, and P. Gao, “Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

X. Shi, Y. Zhang, K. C. Cheung, S. See, X. Wang, H. Qin, and H. Li, “A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

X. Wu, F. Zhu, R. Zhao, and H. Li, “CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

R. Zhang, L. Wang, Y. Qiao, P. Gao, and H. Li, “Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

R. Zhang, L. Wang, Z. Guo, Y. Wang, P. Gao, H. Li, and J. Shi, “Starting from Non-Parametric Networks for 3D Point Cloud Analysis,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

J. Liu, X. Huang, J. Zheng, Y. Liu, and H. Li, “MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

J. Zhou, L. Huang, L. Wang, S. Liu, and H. Li, “Improving Weakly Supervised Temporal Action Localization by Bridging Train-Test Gap in Pseudo Labels,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

J. Mao, S. Shi, X. Wang, and H. Li, “3D object detection for autonomous driving: A comprehensive survey,” International Journal of Computer Vision, Apr. 2023.

Z. Huang, X. Pan, W. Pan, W. Bian, Y. Xu, K. C. Cheung, G. Zhang, and H. Li, “NeuralMarker: A Framework for Learning General Marker Correspondence,” ACM Transactions on Graphics, vol. 41, no. 6, pp. 1–10, Nov. 2022.

K. Sun, S. Wu, Z. Huang, N. Zhang, Q. Wang, and H. Li, “Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields,” In Advances in Neural Information Processing Systems (NeurIPS), 2022.

R. Zhang, Z. Guo, R. Fang, B. Zhao, D. Wang, Y. Qiao, H. Li, and P. Gao, “Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training,” In Advances in Neural Information Processing Systems (NeurIPS), 2022.

J. Pan, Z. Lin, X. Zhu, J. Shao, and H. Li, “ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning,” In Advances in Neural Information Processing Systems (NeurIPS), 2022.

Z. Lin, S. Geng, R. Zhang, P. Gao, G. de Melo, X. Wang, J. Dai, Y. Qiao, and H. Li, “Frozen clip models are efficient video learners,” In Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, vol. 13695, pp. 388–404, Nov. 2022.

X. Chen, S. Shi, B. Zhu, K. C. Cheung, H. Xu, and H. Li, “MPPNet: Multi-frame feature intertwining with proxy points for 3D temporal object detection,” In Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, pp. 680–697, 2022.

D. Li, Y. Zhang, K. C. Cheung, X. Wang, H. Qin, and H. Li, “Learning degradation representations for image Deblurring,” In Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, vol. 13678, pp. 736–753, 2022.

D. Li, Y. Zhang, K. L. Law, X. Wang, H. Qin, and H. Li, “Efficient burst raw denoising with variance stabilization and multi-frequency denoising network,” International Journal of Computer Vision, vol. 130, no. 8, pp. 2060–2080, Jun. 2022.

X. Zhang, Y. Ge, Y. Qiao, and H. Li, “Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification,” in IEEE/ CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 19-25, 2021.

Y. Cai, KY. Lin, C. Zhang, Q.Wang, X. Wang, H. Li. “Learning a Structured Latent Space for Unsupervised Point Cloud Completion,” In IEEE Conference on Computer Vision and Pattern Recognition, 2022.

R. Zhang, Z. Guo, W. Zhang, K. Li, X. Miao, B. Cui, Y. Qiao, P. Gao, H. Li. “PointCLIP: Point Cloud Understanding by CLIP,” In IEEE Conference on Computer Vision and Pattern Recognition, 2022.

Y. Zhang, D. Li, K.L. Law, X. Wang, H. Qin, H. Li. “IDR: Self-Supervised Image Denoising via Iterative Data Refinement,” In IEEE Conference on Computer Vision and Pattern Recognition, 2022.

Y. Xu, K.Y. Lin, G. Zhang, X. Wang, H. Li. “RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization,” In IEEE Conference on Computer Vision and Pattern Recognition, 2022.

L. Huang, L. Wang, H. Li. “Weakly Supervised Temporal Action Localization via Representative Snippet Knowledge Propagation,” In IEEE Conference on Computer Vision and Pattern Recognition, 2022.

Prof. Dahua LIN

Y. Jin, J. Wang, and D. Lin, “Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant,” In Advances in Neural Information Processing Systems (NeurIPS), 2022.

X. Liu, Q. Wu, H. Zhou, Y. Du, W. Wu, D. Lin, and Z. Liu, “Audio-Driven Co-Speech Gesture Video Generation,” In Advances in Neural Information Processing Systems (NeurIPS), 2022.

W. Li, Y. Lai, L. Xu, Y. Xiangli, J. Yu, C. He, G. S. Xia, and D. Lin, “OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

L. Xu, Y. Xiangli, S. Peng, X. Pan, N. Zhao, C. Theobalt, B. Dai, and D. Lin, “Grid-guided Neural Radiance Fields for Large Urban Scenes,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

Y. Jin, J. Wang, and D. Lin, “Multi-level Logit Distillation,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

R. Xu, T. Wang, W. Zhang, R. Chen, J. Cao, J. Pang, and D. Lin, “MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

Z. Lyu, J. Wang, Y. An, Y. Zhang, D. Lin, and B. Dai, “Controllable Mesh Generation Through Sparse Latent Point Diffusion Models,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

L. Xu, Y. Xiangli, S. Peng, X. Pan, N. Zhao, C. Theobalt, B. Dai, and D. Lin, “Grid-guided Neural Radiance Fields for Large Urban Scenes,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

J. Wang, S. Yan, B. Dai, D. Lin. “Scene-aware Generative Network for Human Motion Synthesis,” In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021.

L. Xu, Y. Xiangli, A. Rao, N. Zhao, B. Dai, Z. Liu, D. Lin. “BlockPlanner: City Block Generation with Vectorized Graph Representation,” In International Conference on Computer Vision, 2021

T. Wang, X. Zhu, J. Pang, D. Lin. “Probabilistic and Geometric Depth: Detecting Objects in Perspective,” In Conference on Robot Learning, 2021.

H. Duan, N. Zhao, K.Chen, D. Lin. “TransRank: Self-supervised Video Representation Learning via Ranking-based Transformation Recognition,” In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.

X. Zhu, H. Zhou, F. Hong, T. Wang, Y. Ma, W. Li, H. Li, D. Lin. “Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation,” In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021.

T. Wu, L. Pan, J. Zhang, T. Wang, Z. Liu, D. Lin. “Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion,” In Conference on Neural Information Processing Systems, 2021.

Prof. Eric LO

M. J. Amiri, Z. Lai, L. Patel, B. T. Loo, E. Lo, and W. Zhou, “Saguaro: An Edge Computing-enabled Hierarchical Permissioned Blockchain,” In IEEE International Conference on Data Engineering, Apr. 2023.

Z. Lai, C. Liu, and E. Lo, “When private blockchain meets deterministic database,” In Proceedings of the ACM on Management of Data, vol. 1, no. 1, pp. 1–28, 2023.

Z. Lai, C. Han, C. Liu, P. Zhang, E. Lo, and B. Kao, “Finding Interesting Frames in Deep Video Analytics: A Top-K Approach,” in 2021 ACM SIGMOD/PODS International Conference on Management of Data, Xi’ an, Shaanxi, China, June 20-25, 2021.

Z. Chen, A.W. Fu, M. Jiang, E. Lo, and P. Zhang. “P2H: Efficient Distance Querying on Road Networks by Projected Vertex Separators,” In Proceedings of the 2021 International Conference on Management of Data. Association for Computing Machinery, New York, NY, USA, 313–325. 2021.

Z. Zhong, J. Cui, E. Lo, Z. Li, J. Sun, J. Jia. “Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition,” Computer Vision and Pattern Recognition, arXiv preprint arXiv:2203.11506. 2022 Mar 22.

Prof. Helen MENG

H. Lu, W. Lam, H. Cheng, and H. Meng, “On controlling fallback responses for grounded dialogue generation,” In Findings of the Association for Computational Linguistics: ACL 2022, pages 2591–2601, Dublin, Ireland. Association for Computational Linguistics, May 2022.

K. Li, T. Zhang, L. Tang, J. Li, H. Lu, X. Wu, and H. Meng, “Grounded dialogue generation with cross-encoding re-ranker, grounding span prediction, and passage dropout,” In Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering, pages 123–129, Dublin, Ireland. Association for Computational Linguistics. May 2022.

H. Guo, H. Lu, X. Wu, and H. Meng, “A multi-scale time-frequency spectrogram discriminator for gan-based non-autoregressive TTS,” In Interspeech 2022, 1566-1570. Sep. 2022.

H. Guo, F. Xie, X. Wu, F. K. Soong and H. Meng, “MSMC-TTS: Multi-Stage Multi-Codebook VQ-VAE Based Neural TTS,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 1811-1824, 2023.

M. Chaudhary, B. Dzodzo, S. Huang, C. H. Lo, M. Lyu, L.Y. Nie, J. Xing, T. Zhang, X. Zhang, J. Zhou, H. Cheng, W. Lam, and H. Meng, “Unstructured Knowledge Access in Task-oriented Dialog Modeling using Language Inference, Knowledge Retrieval and Knowledge-Integrative Response Generation,” in 35th AAAI Conference on Artificial Intelligence 2021 Dialog System Technology Challenge Workshop (DSTC9), February 8-9, 2021.

H. Lu, Z. Wu, X. Wu, X. Li, S. Kang, X. Liu, H. Meng. “VAENAR-TTS: Variational Auto-Encoder Based Non-AutoRegressive Text-to-Speech Synthesis,” In Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association, Brno, Czechia, 30 August – 3 September 2021, 3775—3779. 2021.

M. Wu, K. Li and W.K. Leung, H. Meng. “Transformer Based End-to-End Mispronunciation Detection and Diagnosis,” InProc. Interspeech 2021, 3954—3958. 2021.

Y. Deng, W. Zhang, W. Lam, H. Cheng, H. Meng. “User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal – oriented Conversational Systems,” Computation and Language, 2998-3008. 2022.

H. Lu, W. Lam, H. Cheng, H. Meng. “On Controlling Fallback Responses for Grounded Dialogue Generation,” In Findings of the Association for Computational Linguistics: ACL 2022, pages 2591–2601, Dublin, Ireland. Association for Computational Linguistics. 2022.

K. Li, T. Zhang, L. Tang, J. Li, H. Lu, X. Wu, H. Meng. “Grounded Dialogue Generation with Cross-encoding Re-ranker, Grounding Span Prediction, and Passage Dropout,” In Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering, pages 123–129, Dublin, Ireland. Association for Computational Linguistics. 2022.

D. Wang, S. Liu, X. Wu, H. Lu, L. Sun, X. Liu, H. Meng. “Speaker Identity Preservation in Dysarthric Speech Reconstruction by Adversarial Speaker Adaptation,” In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6677-6681). IEEE. 2022.

D. Wang, S. Yang, D. Su, X. Liu, D. Yu, H. Meng. “VCVTS: Multi-speaker Video-to-Speech synthesis via cross-modal knowledge transfer from voice conversion,” In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 7252-7256). IEEE. 2022.

H. Wu, P. Hsu, J. Gao, S. Zhang, S. Huang, J. Kang, Z. Wu, H. Meng, H. Lee. “Adversarial Sample Detection for Speaker Verification by Neural Vocoders,” In ICASSP 2022 – 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 236-240, doi: 10.1109/ICASSP43922.2022.9746900. 2022.

H. Wu, H. Kuo, N. Zheng, K. Hung, H. Lee, Y. Tsao, H. Wang, H. Meng. “Partially Fake Audio Detection by Self-Attention-Based Fake Span Discovery,” In ICASSP 2022 – 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 9236-9240, doi: 10.1109/ICASSP43922.2022.9746162. 2022.

H. Wu, B. Zheng, X. Li, X. Wu, H.Y. Lee and H. Meng. “Characterizing the Adversarial Vulnerability of Speech self-Supervised Learning”, In ICASSP 2022 – 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 3164-3168, doi: 10.1109/ICASSP43922.2022.9747242. 2022.

X. Wu, S. Hu, Z. Wu, X. Liu, H. Meng. “Neural Architecture Search for Speech Emotion Recognition,” In ICASSP 2022 – 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 6902-6906, 10.1109/ICASSP43922.2022.9746155. 2022.

Prof. Yeung YAM

Z. Liu, E. L. Doubrovski, J. M. P. Geraedts, W. Wang, Y. Yam, and C. C. L. Wang, “Photogrammetric Reconstruction of a Stolen Statue,” 2023.

Prof. Liwei WANG

Y. Li, J. Zhao, M. R. Lyu, and L. Wang, “Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation,” In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10551–10564, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. 2022.

Prof. Guoliang XING

S. Shi, J. Cui, Z. Jiang, Z. Yan, G. Xing, J. Niu, and Z. Ouyang, “VIPS: Real-Time Perception Fusion for Infrastructure-Assisted Autonomous Driving,” In Proceedings of the 28th Annual International Conference on Mobile Computing And Networking, Oct. 2022.

X. Shuai, Y. Shen, Y. Tang, S. Shi, L. Ji, and G. Xing, “milliEye: A Lightweight mmWave Radar and Camera Fusion System for Robust Object Detection,” in The ACM/IEEE International Conference on Internet of Things Design and Implementation (IoTDI), May 18-21, 2021.

Z. Zhao, K. Wang, N. Ling, and G. Xing, “EdgeML: An AutoML Framework for Real-Time Deep Learning on the Edge,” in The ACM/IEEE International Conference on Internet of Things Design and Implementation (IoTDI), May 18-21, 2021.

X. Shuai, Y. Shen, S. Jiang, Z. Zhao, W. Lan, G. Xing. “BalanceFL: Addressing Class Imbalance in Long-tail Federated Learning,” Accepted by ACM / IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2022.

Prof. Bolei ZHOU

Y. Liu, J. Zhang, L. Fang, Q. Jiang, and B. Zhou, “Multimodal Motion Prediction with Stacked Transformers,” in IEEE/ CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 19-25, 2021.

C. Yang, Z. Wu, B. Zhou, and S. Lin, “Instance Localization for Self-supervised Detection Pretraining,” in IEEE/ CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 19-25, 2021.

J. Sun, L. Yu, P. Dong, B. Lu, and B. Zhou, “Adversarial Inverse Reinforcement Learning with Self-attention Dynamics Model,” IEEE Robot. Autom. Lett., vol. 6, no. 2, pp. 1880-1886, 2021.

J. Sun, L. Yu, P. Dong, B. Lu, and B. Zhou, “HiABP: Hierarchical Initialized ABP for Unsupervised Representation Learning,” in 35th AAAI Conference on Artificial Intelligence, February 2-9, 2021.

Y. Shen and B. Zhou, “Closed-Form Factorization of Latent Semantics in GANS,” in IEEE/ CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 19-25, 2021.

Y. Xu, Y. Shen, J. Zhu, C. Yang, and B. Zhou, “Generative Hierarchical Features from Synthesizing Images,” in IEEE/ CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 19-25, 2021.

Z. Peng, Q. Li and C. Liu, and B. Zhou. “Safe Driving via Expert Guided Policy Optimization,” In 5th Annual Conference on Robot Learning (CoRL), 2021.

Q. Li, Z. Peng, B. Zhou. “Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization,” In International Conference on Learning Representations (ICLR), 2022.

Z. Peng, Q. Li, K.M. Hui, C. Liu, B. Zhou. “Learning to Simulate Self-driven Particles System with Coordinated Policy Optimization,” In Advances in Neural Information Processing Systems (NeurIPS), 2021.

Q. Li, Z. Peng, L. Feng, Q. Zhang, Z. Xue and B. Zhou, “MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 3461-3475.

X. Liu, Q. Wu, H. Zhou, Y. Xu, R. Qian, X. Lin, X. Zhou, W. Wu, B. Dai, B. Zhou. “Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation,” In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.

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