Publications – Centre for Perceptual and Interactive Intelligence (CPII)

PUBLICATIONS

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

Professor Rosanna Yuen-Yan CHAN

C. Tong, R. Chan, “Gaze-Based Interaction Adaptation for People with Involuntary Head Movements (Student Abstract),” In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, no. 21, pp. 23669-23670, 2024

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.

Professor Shih-Chi CHEN

S. Gu, F. Han, M. Ye, S. C. Chen, “Multi-material 3D Nanofabrication based on Ultrafast Lasers and Swellable Hydrogels,” ASPE 2023, 2023

Z. Deng, S. Gu, Z. Feng, S. C. Chen, “High-throughput Maskless Optical Lithography System based on Digital Holography,” ASPE 2023, 2023

B. Ding, C. Li and S. C. Chen, “Design and Characterization of an XYθZ Nanopositioner,” ASPE 2023, 2023

Y. Wang, Z. Bi, Y. Song, L. Duan, S. C. Chen, “Selective Activation of Photoactivatable Fluorescent Protein based on Binary Holography,” Biomedical Optics Express, Vol.15, no. 5, pp. 3382-3393, 2024

B. Ding, X. Li, C. Li, Y. Li, S. C. Chen, “A Survey on the Mechanical Design for Piezo-actuated Compliant Micro-positioning Stages,” Review of Scientific Instruments, Vol. 94, pp. 101502, 2023

T. Singh, J. Arab, S. C. Chen, “Improvement on Surface Quality of Inconel-718 Slits via Laser Cutting and Wire Electrochemical Machining Processes,” Optics and Laser Technology, Vol. 167, pp. 109637, 2023

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.

Professor Hongsheng LI

F. Hong, L. Kong, H. Zhou, X. Zhu, H. Li, Z. Liu, “Unified 3D and 4D Panoptic Segmentation via Dynamic Shifting Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 46, no. 5, pp. 3480-3495, 2024

K. Sun, S. Wu, N. Zhang, Z. Huang, Q. Wang, H. Li, “CGOF++: Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 46, no. 02, pp. 913-926, 2023

W. Bian, Z. Huang, X. Shi, Y. Dong, Y. Li, H. Li, “Context-PIPs: Persistent Independent Particles Demands Context Features,” NeurlIPS 2023, 2023

X. Yang, L. Xu, S. Yu, Q. Xia, H. Li, S. Zhang, “Geometry-Based End-to-End Segmentation of Coronary Artery in Computed Tomography Angiography,” ICLR 2023 Workshop TML4H, 2023

F. Lu, Y. Xu, G. Chen, H. Li, K. Y. Lin, C. Jiang, “Urban Radiance Field Representation with Deformable Neural Mesh Primitives,” ICCV 2023, 2023

S. Fan, J. Piao, C. Qian, H. Li, K. Y. Lin, “Simulating Fluids in Real-world Still Images,” ICCV 2023, 2023

F. Yan, Q. Da, H. Yi, S. Deng, L. Zhu, Y. Liu, M. Feng, J. Wang, X. Wang, Y. Zhang, W. Zhang, X. Zhang, J. Lin, S. Zhang, C. Wang, “Artificial Intelligence-based Assessment of PD-L1 Expression in Diffuse Large B Cell Lymphoma,” npj Precision Oncology, Vol. no. 76, 2024

R. Zhang, Z. Jiang, Z. Guo, S. Yan, J. Pan, H. Dong, Y. Qiao, P. Gao, H. Li, “Personalize Segment Anything Model with One Shot,” ICLR 2024, 2024

Y. Zhang, X. Shi, D. Li, X. Wang, J. Wang, H. Li, “A Unified Conditional Framework for Diffusion-based Image Restoration,” NeurlIPS 2023, 2023

K. Sun, J. Pan, Y. Ge, H. Li, H. Duan, X. Wu, R. Zhang, A. Zhou, Z. Qin, Y. Wang, J. Dai, Y. Qiao, L. Wang, H. Li, “JourneyDB: A Benchmark for Generative Image Understanding,” NeurlIPS 2023, 2023

Y. Niu, Y. Pu, Z. Yang, X. Li, T. Zhou, J. Ren, S. Hu, H. Li, Y. Liu, “LightZero: A Unified Benchmark for the Monte Carlo Tree Search in General Sequential Decision Scenarios,” NeurlIPS 2023, 2023

L. Huang, K. Lu, G. Song, L. Wang, S. Liu, Y. Liu, H. Li, “Teach-DETR: Better Training DETR with Teachers,” IEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, no. 12, pp. 15759-15771, 2023

X. Wu, K. Sun, F. Zhu, R. Zhao, H. Li, “Human Preference Score: Better Aligning Text-to-Image Models with Human Preference,” ICCV, 2023

T. Ma, X. Yang, H. Zhou, X. Li, B. Shi, J. Liu, Y. Yang, Z. Liu, L. He, Y. Qiao, Y. Li, H. Li, “DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds,” ICCV, 2023

X. Shi, Z. Huang, W. Bian, D. Li, M. Zhang, K. C. Cheung, S. See, H. Qin, J. Dai, H. Li, “Videoflow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation,” ICCV, 2023

J. Liu, T. Wang, B. Liu, Q. Zhang, Y. Liu, H. Li, “GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D Understanding,” ICCV, 2023

A. Zhou, Y. Li, Z. Qin, J. Liu, J. Pan, R. Zhang, R. Zhao, P. Gao, H. Li, “SparseMAE: Sparse Training Meets Masked Autoencoders,” ICCV, 2023

X. Chen, S. Shi, C. Zhang, B. Zhu, Q. Wang, K. C. Cheung, S. See, H. Li, “TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses,” ICCV, 2023

M. Zhang, G. Song, Y. Liu, H. Li, “Decoupled DETR: Spatially Disentangling Localization and Classification for Improved End-to-End Object Detection,” ICCV, 2023

J. Yao, C. Li, K. Sun, Y. Cai, H. Li, W. Ouyang, H. Li, “NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Space,” ICCV, 2023

R. Zhang, H. Qiu, T. Wang, Z. Guo, Z. Cui, Y. Qiao, P. Gao, H. Li, “MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection.” ICCV, 2023

C. Tao, D. Gu, R. Huang, L. Zhou, Z. Hu, Y. Chen, X. Zhang, “Hippocampus Segmentation after Brain Tumor Resection via Postoperative Region Synthesis,” BMC Medical Imaging, 23(1), 1–142. 2023.

R. Zhang, J. Han, C. Liu, A. Zhou, P. Lu, Y. Qiao, H. Li, P. Gao. “LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-initialized Attention,” ICLR, 2024

Q. Chang, Z. Yan, M. Zhou, H. Zhang, X. He, H. Zhang, L. Baskaran, S. Al’Aref, H. Li, S. Zhang, D. N. Metaxas, “Mining multi-center heterogeneous medical data with distributed synthetic learning,” Nature Communications, Vol. 14, pp. 5510, 2023.

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.

Professor Dahua LIN

R. Qian, S. Ding, X. Liu, D. Lin, “Semantics Meets Temporal Correspondence: Self-supervised Object-centric Learning in Videos,” ICCV 2023, 2023

Y. Guo, C. Yang, A. Rao, Z. Liang, Y. Wang, Y. Qiao, M. Agrawala, D. Lin, B. Dai, “AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning,” ICLR 2024, 2024

Y. Li, L. Xu, Y. Xiangli, Z. Wang, D. Lin, B. Dai, “MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond,” ICCV 2023, 2023

J. Wang, P. Zhang, T. Chu, Y. Cao, Y. Zhou, T. Wu, B. Wang, C. He, D. Lin, “V3Det: Vast Vocabulary Visual Detection Dataset,” ICCV 2023, 2023

Z. Lyu, J. Wang, Y. An, Y. Zhang, D. Lin, B. Dai, “Controllable Mesh Generation Through Sparse Latent Point Diffusion Models,” CVPR 2023, 2023

R. Xu, T. Wang, W. Zhang, R. Chen, J. Cao, J. Pang, D. Lin, “MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training,” CVPR 2023, 2023

A. Rao, X. Jiang, Y. Guo, L. Xu, L. Yang, L. Jin, D. Lin, B. Dai, “Dynamic Storyboard Generation in an Engine-based Virtual Environment for Video Production,” SIGGRAPH 2023, 2023

T. Wu, J. Zhang, X. Fu, Y. Wang, J. Ren, L. Pan, W. Wu, L. Yang, J. Wang, C. Qian, D. Lin, Z. Liu, “OmniObject3D: Large-vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation,” CVPR 2023, 2023

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.

Professor Eric LO

Y. Zhang, J. Xing, E. Lo, J. Jia, “Real-World Image Variation by Aligning Diffusion Inversion Chain,” NeurlIPS 2023, 2023

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.

Professor Helen MENG

H. Lu, X. Wu, Z. Wu, H. Meng, “Speech TripleNet: End-to-End Disentangled Speech Representation Learning for Content, Trimbre and Prosody,” In MM’23: the 31^st ACM International Conference on Multimedia, pp. 2829-2837, 2023

H. Lu, X. Wu, H. Guo, S. Liu, Z. Wu, H. Meng, “Unifying One-Shot Voice Conversion and Cloning with Disentangled Speech Representations,” In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 11141-11145, 2024

X. Chen, Y. Wang, X. Wu, D. Wang, Z. Wu, X. Liu, H. Meng, “Exploiting Audio-Visual Features with Pretrained AV-HuBERT for Multi-Modal Dysarthric Speech Reconstruction,” In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 12341-12345, 2024

X. Zhang, B. Peng, K. Li, J. Zhou, H. Meng, “SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting,” In Findings of the Association for Computational Linguistics: EMNLP 2023 (EMNLP Findings 2023), pp. 13348-13369, 2023

H. Luo, T. Zhang, Y. S. Chuang, Y. Gong, Y. Kim, X. Wu, H. Meng, J. Glass, “Search Augmented Instruction Learning,” In Findings of the Association for Computational Linguistics: EMNLP 2023 (EMNLP Findings 2023), pp.3717-3729, 2023

Y. Li, P. Liu, X. Wu, H. Meng, “PunCantonese: A Benchmark Corpus for Low-Resource Cantonese Punctuation Restoration from Speech Transcripts,” In Interspeech 2023, pp. 2183-2187), 2023

T. Zhang, L. Tang, W. Fang, H. Luo, X. Wu, H. Meng and J. Glass, “ConvRGX: Recognition, Generation, and Extraction for Self-trained Conversational Question Answering,” In DialDoc 2023, 2023

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.

Professor Yeung YAM

X. Chen, C. Dai, B. Sun, C. P. Lam, G. Fang, Y. Yam, C. C. L. Wang, “Weaving-Based Freeform Sensing Interface: Design and Fabrication,” ICRA 2024 Workshop, 2024

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.

Professor Liwei WANG

Y. Li, S. Liang, M. R. Lyu, L. Wang, “Making Long-Context Language Models Better Multi-Hop Reasoners,” In Proceedings of the 62^nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), Vol. 1: Long Papers, pp. 2462-2475, 2024

S. Huang, J. Zhao, Y. Li, L. Wang, “Learning Preference Model for LLMs via Automatic Preference Data Generation,” In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023), pp. 9187-9199, 2023

D. Zheng, S. Huang, L. Zhao, Y. Zhong, L. Wang, “Towards Learning a Generalist Model for Embodied Navigation,” CVPR 2024, pp. 13624-13634, 2024

Y. Li, J. Zhao, D. Zheng, Z. Y. Hu, Z. Chen, X. Su, Y. Huang, S. Huang, D. Lin, M. R. Lyu, L. Wang, “CLEVA: Chinese Language Models EVAluation Platform,” In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 186-217, 2023

Y. Huang, Y. Li, Y. Xu, L. Zhang, R. Gan, J. Zhang, L. Wang, “MVP-Tuning: Multi-View Knowledge Retrieval with Prompt Tuning for Commonsense Reasoning,“ In Proceedings of the 61^st Annual Meeting of the Association for Computational Linguistics (ACL 2023). Vol.1: Long Papers, pp. 13417-13432, 2023

Z. Y. Hu, Y. Li, M. R. Lyu, L. Wang, “VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity Control,” ICCV 2023, 2023

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.

Professor Guoliang XING

J. Cui, S. Shi, Y. He, J. Niu, G. Xing, Z. Ouyang, “VILAM: Infrastructure-assisted 3D Visual Localization and Mapping for Autonomous Driving,” In Proceedings of the 21^st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24), pp. 1831-1845, 2024

X. Song, Y. Hua, Y. Yang, G. Xing, F. Liu, L. Xu, T. Song, “Distributed Resource Allocation with Federated Learning for Delay-Sensitive IoV Services,” In IEEE Transactions on Vehicular Technology, Vol. 73, no. 3, pp. 4326-4336, 2024

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.

Professor 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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