Zhe Li

Orcid: 0009-0008-3896-7694

According to our database1, Zhe Li authored at least 14 papers between 2023 and 2025.

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Bibliography

2025
Tensor-empowered Incomplete Multimodal Learning with Modality Reconstruction for Edge Intelligence.
ACM Trans. Multim. Comput. Commun. Appl., August, 2025

Interpretable Multimodal Tucker Fusion Model With Information Filtering for Multimodal Sentiment Analysis.
IEEE Trans. Comput. Soc. Syst., June, 2025

Tensor-Empowered Hardware-Friendly Lightweight Deep Neural Networks for Vehicular Edge Computing.
IEEE Trans. Consumer Electron., February, 2025

LaMP: Language-Motion Pretraining for Motion Generation, Retrieval, and Captioning.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

2024
MCMat: Multiview-Consistent and Physically Accurate PBR Material Generation.
CoRR, 2024

MulSMo: Multimodal Stylized Motion Generation by Bidirectional Control Flow.
CoRR, 2024

DCS-Net: Pioneering Leakage-Free Point Cloud Pretraining Framework with Global Insights.
CoRR, 2024

TD3D: Tensor-based Discrete Diffusion Process for 3D Shape Generation.
Proceedings of the IEEE International Conference on Multimedia and Expo, 2024

Capturing Detail Variations for Lightweight Neural Radiance Fields.
Proceedings of the IEEE International Conference on Acoustics, 2024

MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

General Point Model Pretraining with Autoencoding and Autoregressive.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
General Point Model with Autoencoding and Autoregressive.
CoRR, 2023

Enhancing Sentence Representation with Visually-supervised Multimodal Pre-training.
Proceedings of the 31st ACM International Conference on Multimedia, 2023


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