1 code implementation • 23 Aug 2024 • Tianfei Zhou, Wang Xia, Fei Zhang, Boyu Chang, Wenguan Wang, Ye Yuan, Ender Konukoglu, Daniel Cremers
This survey seeks to fill this gap by providing a thorough review of cutting-edge research centered around FM-driven image segmentation.
no code implementations • 19 Aug 2024 • Chu Sun, Fei Zhang, Huafeng Xiao, Na Wang, Jikai Chen
However, the existing MMC impedance models usually lack explicit expressions and general modeling procedure for different control strategies.
no code implementations • CVPR 2024 • Jinxiang Liu, Yikun Liu, Fei Zhang, Chen Ju, Ya zhang, Yanfeng Wang
NFs, temporally adjacent to the labeled frame, often contain rich motion information that assists in the accurate localization of sounding objects.
1 code implementation • NeurIPS 2023 • Chaofan Ma, Yuhuan Yang, Chen Ju, Fei Zhang, Ya zhang, Yanfeng Wang
The results show the superior performance of attribute decomposition-aggregation.
no code implementations • 14 Jul 2023 • Fei Zhang, Yunjie Ye, Lei Feng, Zhongwen Rao, Jieming Zhu, Marcus Kalander, Chen Gong, Jianye Hao, Bo Han
In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle from the demanding accurate labeling process.
no code implementations • 5 Jul 2023 • Yuhuan Yang, Chaofan Ma, Chen Ju, Fei Zhang, Jiangchao Yao, Ya zhang, Yanfeng Wang
To be specific, unlike the straightforward combination of bi-modal clues, we decompose the high-level language information as multi-aspect prototypes and aggregate the low-level visual information as more semantic prototypes, on basis of which, a fine-grained complementary fusion makes the multi-modal prototypes more powerful and accurate to promote the prediction.
no code implementations • 1 Jun 2023 • Reza Shirkavand, Fei Zhang, Heng Huang
This work highlights the potential of deep learning techniques, specifically transformer-based models, in revolutionizing the healthcare industry's approach to postoperative care.
1 code implementation • ICCV 2023 • Boyang Li, Yingqian Wang, Longguang Wang, Fei Zhang, Ting Liu, Zaiping Lin, Wei An, Yulan Guo
The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters.
no code implementations • 17 Mar 2023 • Chaofan Ma, Yuhuan Yang, Chen Ju, Fei Zhang, Jinxiang Liu, Yu Wang, Ya zhang, Yanfeng Wang
However, the challenges exist as there is one structural difference between generative and discriminative models, which limits the direct use.
no code implementations • 4 Mar 2023 • Jiren Mai, Fei Zhang, Junjie Ye, Marcus Kalander, Xian Zhang, Wankou Yang, Tongliang Liu, Bo Han
Motivated by this simple but effective learning pattern, we propose a General-Specific Learning Mechanism (GSLM) to explicitly drive a coarse-grained CAM to a fine-grained pseudo mask.
3 code implementations • ICLR 2022 • Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama
As the first contribution, we empirically show that the class activation map (CAM), a simple technique for discriminating the learning patterns of each class in images, is surprisingly better at making accurate predictions than the model itself on selecting the true label from candidate labels.
1 code implementation • ICCV 2021 • Fei Zhang, Chaochen Gu, Chenyue Zhang, Yuchao Dai
Therefore, a CAM with more information related to object seeds can be obtained by narrowing down the gap between the sum of CAMs generated by the CP Pair and the original CAM.
no code implementations • 23 Jul 2021 • Fei Zhang, Chaochen Gu, Feng Yang
Deep Q Network (DQN) has several limitations when applied in planning a path in environment with a number of dilemmas according to our experiment.
1 code implementation • 25 May 2020 • Fei Zhang, Patrick P. K. Chan, Battista Biggio, Daniel S. Yeung, Fabio Roli
Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion and malware detection, although their security against well-crafted attacks that aim to evade detection by manipulating data at test time has not yet been thoroughly assessed.
no code implementations • 18 May 2016 • Ke Yang, Yong Dou, Shaohe Lv, Fei Zhang, Qi Lv
This study focuses on human recognition with gait feature obtained by Kinect and shows that gait feature can effectively distinguish from different human beings through a novel representation -- relative distance-based gait features.