no code implementations • 25 Mar 2024 • Shilong Zhang, Lianghua Huang, Xi Chen, Yifei Zhang, Zhi-Fan Wu, Yutong Feng, Wei Wang, Yujun Shen, Yu Liu, Ping Luo
This work presents FlashFace, a practical tool with which users can easily personalize their own photos on the fly by providing one or a few reference face images and a text prompt.
no code implementations • ICCV 2023 • Shiyue Cao, Yueqin Yin, Lianghua Huang, Yu Liu, Xin Zhao, Deli Zhao, Kaiqi Huang
Vector-quantized image modeling has shown great potential in synthesizing high-quality images.
2 code implementations • 18 Jul 2023 • Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, Hengshuang Zhao
This work presents AnyDoor, a diffusion-based image generator with the power to teleport target objects to new scenes at user-specified locations in a harmonious way.
no code implementations • 20 Jun 2023 • Zhantao Yang, Ruili Feng, Han Zhang, Yujun Shen, Kai Zhu, Lianghua Huang, Yifei Zhang, Yu Liu, Deli Zhao, Jingren Zhou, Fan Cheng
Diffusion models, which employ stochastic differential equations to sample images through integrals, have emerged as a dominant class of generative models.
6 code implementations • 20 Feb 2023 • Lianghua Huang, Di Chen, Yu Liu, Yujun Shen, Deli Zhao, Jingren Zhou
Recent large-scale generative models learned on big data are capable of synthesizing incredible images yet suffer from limited controllability.
no code implementations • CVPR 2023 • Han Zhang, Ruili Feng, Zhantao Yang, Lianghua Huang, Yu Liu, Yifei Zhang, Yujun Shen, Deli Zhao, Jingren Zhou, Fan Cheng
Diffusion models, which learn to reverse a signal destruction process to generate new data, typically require the signal at each step to have the same dimension.
no code implementations • 16 Oct 2022 • Yueqin Yin, Lianghua Huang, Yu Liu, Kaiqi Huang
In this work, we first design a group of mechanisms to simulate generative artifacts of popular generators (i. e., GANs, autoregressive models, and diffusion models), given real images.
1 code implementation • 26 Feb 2022 • Shiyu Hu, Xin Zhao, Lianghua Huang, Kaiqi Huang
Finally, we design a scientific evaluation procedure using human capabilities as the baseline to judge tracking intelligence.
1 code implementation • CVPR 2021 • Lianghua Huang, Yu Liu, Bin Wang, Pan Pan, Yinghui Xu, Rong Jin
A key challenge in self-supervised video representation learning is how to effectively capture motion information besides context bias.
no code implementations • 9 Feb 2021 • Yu Liu, Lianghua Huang, Pan Pan, Bin Wang, Yinghui Xu, Rong Jin
However, scaling up the classification task from thousands of semantic labels to millions of instance labels brings specific challenges including 1) the large-scale softmax computation; 2) the slow convergence due to the infrequent visiting of instance samples; and 3) the massive number of negative classes that can be noisy.
1 code implementation • 18 Dec 2019 • Lianghua Huang, Xin Zhao, Kaiqi Huang
Specifically, we propose GlobalTrack, a pure global instance search based tracker that makes no assumption on the temporal consistency of the target's positions and scales.
no code implementations • ICCV 2019 • Lianghua Huang, Xin Zhao, Kaiqi Huang
Then a meta-learner is adopted for the detector to fast learn and adapt a target-distractor classifier online.
2 code implementations • 29 Oct 2018 • Lianghua Huang, Xin Zhao, Kaiqi Huang
(5) Finally, we develop a comprehensive platform for the tracking community that offers full-featured evaluation toolkits, an online evaluation server, and a responsive leaderboard.