1 code implementation • 1 Apr 2024 • Yixuan Zhu, Ao Li, Yansong Tang, Wenliang Zhao, Jie zhou, Jiwen Lu
The recovery of occluded human meshes presents challenges for current methods due to the difficulty in extracting effective image features under severe occlusion.
no code implementations • 19 Dec 2023 • Haoyu Ma, Shahin Mahdizadehaghdam, Bichen Wu, Zhipeng Fan, YuChao Gu, Wenliang Zhao, Lior Shapira, Xiaohui Xie
Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control.
1 code implementation • 3 Dec 2023 • Zengyi Qin, Wenliang Zhao, Xumin Yu, Xin Sun
The voice styles are not directly copied from and constrained by the style of the reference speaker.
2 code implementations • ICCV 2023 • Wenliang Zhao, Yongming Rao, Zuyan Liu, Benlin Liu, Jie zhou, Jiwen Lu
In this paper, we propose VPD (Visual Perception with a pre-trained Diffusion model), a new framework that exploits the semantic information of a pre-trained text-to-image diffusion model in visual perception tasks.
Ranked #7 on Referring Expression Segmentation on RefCoCo val
1 code implementation • CVPR 2023 • Shuai Shen, Wenliang Zhao, Zibin Meng, Wanhua Li, Zheng Zhu, Jie zhou, Jiwen Lu
In this way, the proposed DiffTalk is capable of producing high-quality talking head videos in synchronization with the source audio, and more importantly, it can be naturally generalized across different identities without any further fine-tuning.
1 code implementation • CVPR 2023 • Wenliang Zhao, Yongming Rao, Weikang Shi, Zuyan Liu, Jie zhou, Jiwen Lu
Unlike previous work that relies on carefully designed network architectures and loss functions to fuse the information from the source and target faces, we reformulate the face swapping as a conditional inpainting task, performed by a powerful diffusion model guided by the desired face attributes (e. g., identity and landmarks).
7 code implementations • 28 Jul 2022 • Yongming Rao, Wenliang Zhao, Yansong Tang, Jie zhou, Ser-Nam Lim, Jiwen Lu
In this paper, we show that the key ingredients behind the vision Transformers, namely input-adaptive, long-range and high-order spatial interactions, can also be efficiently implemented with a convolution-based framework.
Ranked #20 on Semantic Segmentation on ADE20K
1 code implementation • 4 Jul 2022 • Yongming Rao, Zuyan Liu, Wenliang Zhao, Jie zhou, Jiwen Lu
We extend our method to hierarchical models including CNNs and hierarchical vision Transformers as well as more complex dense prediction tasks that require structured feature maps by formulating a more generic dynamic spatial sparsification framework with progressive sparsification and asymmetric computation for different spatial locations.
no code implementations • 26 Mar 2022 • Sha Yuan, Hanyu Zhao, Shuai Zhao, Jiahong Leng, Yangxiao Liang, Xiaozhi Wang, Jifan Yu, Xin Lv, Zhou Shao, Jiaao He, Yankai Lin, Xu Han, Zhenghao Liu, Ning Ding, Yongming Rao, Yizhao Gao, Liang Zhang, Ming Ding, Cong Fang, Yisen Wang, Mingsheng Long, Jing Zhang, Yinpeng Dong, Tianyu Pang, Peng Cui, Lingxiao Huang, Zheng Liang, HuaWei Shen, HUI ZHANG, Quanshi Zhang, Qingxiu Dong, Zhixing Tan, Mingxuan Wang, Shuo Wang, Long Zhou, Haoran Li, Junwei Bao, Yingwei Pan, Weinan Zhang, Zhou Yu, Rui Yan, Chence Shi, Minghao Xu, Zuobai Zhang, Guoqiang Wang, Xiang Pan, Mengjie Li, Xiaoyu Chu, Zijun Yao, Fangwei Zhu, Shulin Cao, Weicheng Xue, Zixuan Ma, Zhengyan Zhang, Shengding Hu, Yujia Qin, Chaojun Xiao, Zheni Zeng, Ganqu Cui, Weize Chen, Weilin Zhao, Yuan YAO, Peng Li, Wenzhao Zheng, Wenliang Zhao, Ziyi Wang, Borui Zhang, Nanyi Fei, Anwen Hu, Zenan Ling, Haoyang Li, Boxi Cao, Xianpei Han, Weidong Zhan, Baobao Chang, Hao Sun, Jiawen Deng, Chujie Zheng, Juanzi Li, Lei Hou, Xigang Cao, Jidong Zhai, Zhiyuan Liu, Maosong Sun, Jiwen Lu, Zhiwu Lu, Qin Jin, Ruihua Song, Ji-Rong Wen, Zhouchen Lin, LiWei Wang, Hang Su, Jun Zhu, Zhifang Sui, Jiajun Zhang, Yang Liu, Xiaodong He, Minlie Huang, Jian Tang, Jie Tang
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
1 code implementation • CVPR 2022 • Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie zhou, Jiwen Lu
In this work, we present a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP.
1 code implementation • ICCV 2021 • Xumin Yu, Yongming Rao, Wenliang Zhao, Jiwen Lu, Jie zhou
Assessing action quality is challenging due to the subtle differences between videos and large variations in scores.
Ranked #2 on Action Quality Assessment on MTL-AQA
1 code implementation • ICCV 2021 • Wenliang Zhao, Yongming Rao, Ziyi Wang, Jiwen Lu, Jie zhou
Our method is model-agnostic, which can be applied to off-the-shelf backbone networks and metric learning methods.
Ranked #16 on Metric Learning on CUB-200-2011
4 code implementations • NeurIPS 2021 • Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie zhou
Recent advances in self-attention and pure multi-layer perceptrons (MLP) models for vision have shown great potential in achieving promising performance with fewer inductive biases.
Ranked #9 on Image Classification on Stanford Cars (using extra training data)
1 code implementation • NeurIPS 2021 • Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie zhou, Cho-Jui Hsieh
Based on this observation, we propose a dynamic token sparsification framework to prune redundant tokens progressively and dynamically based on the input.
Ranked #3 on Efficient ViTs on ImageNet-1K (With LV-ViT-S)
5 code implementations • 9 Jun 2018 • Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, Quan Lu
The data involved in CTR prediction are typically multi-field categorical data, i. e., every feature is categorical and belongs to one and only one field.