no code implementations • 10 Aug 2023 • Jun Ma, Ronald Xie, Shamini Ayyadhury, Cheng Ge, Anubha Gupta, Ritu Gupta, Song Gu, Yao Zhang, Gihun Lee, Joonkee Kim, Wei Lou, Haofeng Li, Eric Upschulte, Timo Dickscheid, José Guilherme de Almeida, Yixin Wang, Lin Han, Xin Yang, Marco Labagnara, Vojislav Gligorovski, Maxime Scheder, Sahand Jamal Rahi, Carly Kempster, Alice Pollitt, Leon Espinosa, Tâm Mignot, Jan Moritz Middeke, Jan-Niklas Eckardt, Wangkai Li, Zhaoyang Li, Xiaochen Cai, Bizhe Bai, Noah F. Greenwald, David Van Valen, Erin Weisbart, Beth A. Cimini, Trevor Cheung, Oscar Brück, Gary D. Bader, Bo wang
This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
2 code implementations • 6 Jun 2023 • Youcai Zhang, Xinyu Huang, Jinyu Ma, Zhaoyang Li, Zhaochuan Luo, Yanchun Xie, Yuzhuo Qin, Tong Luo, Yaqian Li, Shilong Liu, Yandong Guo, Lei Zhang
We are releasing the RAM at \url{https://recognize-anything. github. io/} to foster the advancements of large models in computer vision.
no code implementations • 4 Dec 2021 • Jinhong Lin, Zhaoyang Li
Knowledge distillation aims at transferring the knowledge from a large teacher model to a small student model with great improvements of the performance of the student model.
1 code implementation • CVPR 2021 • Gege Gao, Huaibo Huang, Chaoyou Fu, Zhaoyang Li, Ran He
In this work, we propose a novel information disentangling and swapping network, called InfoSwap, to extract the most expressive information for identity representation from a pre-trained face recognition model.
no code implementations • CVPR 2021 • Jia Li, Zhaoyang Li, Jie Cao, Xingguang Song, Ran He
In this work, we propose a novel two-stage framework named FaceInpainter to implement controllable Identity-Guided Face Inpainting (IGFI) under heterogeneous domains.
no code implementations • 1 Feb 2021 • WeiJie Chen, Yilu Guo, Shicai Yang, Zhaoyang Li, Zhenxin Ma, Binbin Chen, Long Zhao, Di Xie, ShiLiang Pu, Yueting Zhuang
Therefore, it yields our attention to suppress false positive in each target domain in an unsupervised way.
1 code implementation • ICCV 2019 • Sicong Tang, Feitong Tan, Kelvin Cheng, Zhaoyang Li, Siyu Zhu, Ping Tan
To achieve this goal, we separate the depth map into a smooth base shape and a residual detail shape and design a network with two branches to regress them respectively.
no code implementations • 8 Nov 2016 • Pichao Wang, Zhaoyang Li, Yonghong Hou, Wanqing Li
Recently, Convolutional Neural Networks (ConvNets) have shown promising performances in many computer vision tasks, especially image-based recognition.
no code implementations • 1 Feb 2016 • Pichao Wang, Zhaoyang Li, Yonghong Hou, Wanqing Li
This paper proposes a new framework for RGB-D-based action recognition that takes advantages of hand-designed features from skeleton data and deeply learned features from depth maps, and exploits effectively both the local and global temporal information.