Publications
You can also find my articles on my Google Scholar profile.
Z. Wang, Z. Li, A. Mandlekar, Z. Xu, J. Fan, Y. Narang, L. Fan, Y. Zhu, Y. Balaji, M. Zhou, M.-Y. Liu, and Y. Zeng, “One-step diffusion policy: Fast visuomotor policies via diffusion distillation,” arXiv preprint arXiv:2410.21257, 2024. [pdf] [Porject Page] 
Y. Zeng, V. M. Patel, H. Wang, X. Huang, T. Wang, M. Liu, Y. Balaji, “Jedi: Joint-image diffusion models for finetuning-free personalized text-to-image generation,” CVPR, 2024. [pdf] [Project Page] 
- Y. Mei, Y. Zeng, H. Zhang, Z. Shu, H. Zhang, S. Bi, J. Zhang, H. Jung, V. Patel, “Holo-relighting: Controllable volumetric portrait relighting from a single image,” CVPR, 2024. [Paper] [Project]
- H. Zhao, Y. Zeng, H. Lu, and L. Wang, “Large occluded human image completion via image-prior cooperating,” AAAI, 2024. [coming soon]
- Y. Zeng, Z. Lin, J. Zhang, Q. Liu, J. Collomosse, J. Kuen, V. Patel, “Scenecomposer: Any-level semantic image synthesis,” CVPR, 2023 (Hightlight, top 2.5% ). [Paper] [Project]
- Y. Zeng, Z. Lin, and V. M. Patel, “Sketchedit: Mask-free local image manipulation with partial sketches,” CVPR, 2022. [Paper] [Project] [Code]
- Y. Zeng, M. Zhou, Y. Xue, and V. M. Patel, “Securing deep generative models with universal adversarial signature,” arXiv preprint arXiv:2305.16310, 2023. [Paper] [Code]
- Y. Zeng, Z. Lin, and V. M. Patel, “Shape-guided object inpainting,” arXiv preprint arXiv:2204.07845. [Paper]
- J. Shang, Y. Zeng, X. Qiao, et al., “Jr2net: Joint monocular 3d face reconstruction and reenactment,” AAAI, 2023 (Oral presentation). [Paper]
- S. Cai, Y. Zeng, S. Yang, X. Jia, H. Lu, and Y. He, “Deformable dynamic sampling and dynamic predictable mask mining for image inpainting,” IEEE TNNLS, 2022. [Paper]
- H. Zhang, Y. Zeng, H. Lu, L. Zhang, J. Li, and J. Qi, “Learning to detect salient object with multi-source weak supervision,” IEEE TPAMI, 2021. [Paper] [Code]
- Y. Zeng, Z. Lin, H. Lu, and V. M. Patel, “Cr-fill: Generative image inpainting with auxiliary contextual reconstruction,” ICCV, 2021. [Paper] [Code]
- Y. Zeng, Z. Lin, J. Yang, J. Zhang, E. Shechtman, and H. Lu, “High-resolution image inpainting with iterative confidence feedback and guided upsampling,” ECCV, 2020. [Paper] [Project]
- E. Ntavelis, A. Romero, S. Bigdeli, et al., “Aim 2020 challenge on image extreme inpainting,” in ECCV, 2020.
- Y. Zhuge, Y. Zeng, and H. Lu, “Deep embedding features for salient object detection,” AAAI, 2019.
- Y. Zeng, Y. Zhuge, H. Lu, and L. Zhang, “Joint learning of saliency detection and weakly supervised semantic segmentation,” ICCV, 2019. [Paper] [Code]
- Y. Zeng, Y. Zhuge, H. Lu, L. Zhang, M. Qian, and Y. Yu, “Multi-source weak supervision for saliency detection,” CVPR, 2019. [Paper] [Code]
- Y. Zeng, H. Lu, L. Zhang, M. Feng, and A. Borji, “Learning to promote saliency detectors,” CVPR, 2018. [Paper)] [Code)]
- Y. Zeng, M. Feng, H. Lu, G. Yang, and A. Borji, “An unsupervised game-theoretic approach to saliency detection,” IEEE TIP, 2018. [Paper] [Code]
- Y. Zeng, H. Lu, and A. Borji, “Statistics of deep generated images,” arXiv preprint arXiv:1708.02688, 2017. [Paper]
