| Masked autoencoders for point cloud self-supervised learning Y Pang, W Wang, FEH Tay, W Liu, Y Tian, L Yuan European conference on computer vision, 604-621, 2022 | 416 | 2022 |
| Moe-llava: Mixture of experts for large vision-language models B Lin, Z Tang, Y Ye, J Cui, B Zhu, P Jin, J Zhang, M Ning, L Yuan arXiv preprint arXiv:2401.15947, 2024 | 84 | 2024 |
| Languagebind: Extending video-language pretraining to n-modality by language-based semantic alignment B Zhu, B Lin, M Ning, Y Yan, J Cui, HF Wang, Y Pang, W Jiang, J Zhang, ... arXiv preprint arXiv:2310.01852, 2023 | 77 | 2023 |
| Repaint123: Fast and high-quality one image to 3d generation with progressive controllable 2d repainting J Zhang, Z Tang, Y Pang, X Cheng, P Jin, Y Wei, W Yu, M Ning, L Yuan arXiv preprint arXiv:2312.13271, 2023 | 13 | 2023 |
| Envision3D: One Image to 3D with Anchor Views Interpolation Y Pang, T Jia, Y Shi, Z Tang, J Zhang, X Cheng, X Zhou, FEH Tay, L Yuan arXiv preprint arXiv:2403.08902, 2024 | 1 | 2024 |
| Abnormal wedge bond detection using convolutional autoencoders in industrial vision systems JY Wu, Y Pang, X Li, WF Lu 2022 International Conference on Electrical, Computer, Communications and …, 2022 | 1 | 2022 |
| Cycle3D: High-quality and Consistent Image-to-3D Generation via Generation-Reconstruction Cycle Z Tang, J Zhang, X Cheng, W Yu, C Feng, Y Pang, B Lin, L Yuan arXiv preprint arXiv:2407.19548, 2024 | | 2024 |
| Appendix for “Masked Autoencoders for Point Cloud Self-supervised Learning” Y Pang, W Wang, FEH Tay, W Liu, Y Tian, L Yuan | | |
