1 code implementation • 20 Mar 2024 • Fu-Yun Wang, Xiaoshi Wu, Zhaoyang Huang, Xiaoyu Shi, Dazhong Shen, Guanglu Song, Yu Liu, Hongsheng Li
We introduce MOTIA Mastering Video Outpainting Through Input-Specific Adaptation, a diffusion-based pipeline that leverages both the intrinsic data-specific patterns of the source video and the image/video generative prior for effective outpainting.
1 code implementation • 1 Feb 2024 • Fu-Yun Wang, Zhaoyang Huang, Xiaoyu Shi, Weikang Bian, Guanglu Song, Yu Liu, Hongsheng Li
We validate the proposed strategy in image-conditioned video generation and layout-conditioned video generation, all achieving top-performing results.
no code implementations • 29 Jan 2024 • Xiaoyu Shi, Zhaoyang Huang, Fu-Yun Wang, Weikang Bian, Dasong Li, Yi Zhang, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, Jifeng Dai, Hongsheng Li
For the first stage, we propose a diffusion-based motion field predictor, which focuses on deducing the trajectories of the reference image's pixels.
1 code implementation • 29 May 2023 • Fu-Yun Wang, Wenshuo Chen, Guanglu Song, Han-Jia Ye, Yu Liu, Hongsheng Li
To address this challenge, we introduce a novel paradigm dubbed as Gen-L-Video, capable of extending off-the-shelf short video diffusion models for generating and editing videos comprising hundreds of frames with diverse semantic segments without introducing additional training, all while preserving content consistency.
2 code implementations • 10 Apr 2022 • Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan
The ability to learn new concepts continually is necessary in this ever-changing world.
Ranked #1 on Incremental Learning on ImageNet100 - 20 steps
1 code implementation • CVPR 2022 • Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, ShiLiang Pu, De-Chuan Zhan
Forward compatibility requires future new classes to be easily incorporated into the current model based on the current stage data, and we seek to realize it by reserving embedding space for future new classes.
Ranked #3 on Few-Shot Class-Incremental Learning on CIFAR-100
1 code implementation • 23 Dec 2021 • Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, De-Chuan Zhan
Traditional machine learning systems are deployed under the closed-world setting, which requires the entire training data before the offline training process.