no code implementations • 27 Jul 2024 • Shuran Ma, Weiying Xie, Daixun Li, Haowei Li, Yunsong Li
To comprehensively address this challenge, we introduce FedCD (Cross-Domain Invariant Federated Learning), an overall optimization framework at both the local and global levels.
1 code implementation • CVPR 2024 • Boheng Li, Yishuo Cai, Haowei Li, Feng Xue, Zhifeng Li, Yiming Li
Model quantization is widely used to compress and accelerate deep neural networks.
1 code implementation • 26 Mar 2024 • Yihao Liu, Jiaming Zhang, Andres Diaz-Pinto, Haowei Li, Alejandro Martin-Gomez, Amir Kheradmand, Mehran Armand
To this end, a unified platform helps push the boundary of the foundation model for medical images, facilitating the use, modification, and validation of SAM and its variants in medical image segmentation.
1 code implementation • CVPR 2024 • Weiying Xie, Haowei Li, Jitao Ma, Yunsong Li, Jie Lei, Donglai Liu, Leyuan Fang
In this paper we propose Joint Sparsification-Quantization (JointSQ) inspired by the discovery that sparsification can be treated as 0-bit quantization regardless of architectures.
1 code implementation • 20 Nov 2023 • Boni Hu, Lin Chen, Runjian Chen, Shuhui Bu, Pengcheng Han, Haowei Li
Visual geolocalization is a cost-effective and scalable task that involves matching one or more query images, taken at some unknown location, to a set of geo-tagged reference images.
no code implementations • 27 Jun 2023 • Haowei Li, Wenqing Yan, Du Liu, Long Qian, Yuxing Yang, Yihao Liu, Zhe Zhao, Hui Ding, Guangzhi Wang
The head surface is reconstructed using depth data for spatial registration, avoiding fixing tracking targets rigidly on the patient's skull.
no code implementations • 25 May 2022 • Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
1 code implementation • 4 Feb 2020 • Tong Liu, Zhaowei Chen, Yi Yang, Zehao Wu, Haowei Li
Nowadays, deep learning techniques are widely used for lane detection, but application in low-light conditions remains a challenge until this day.