no code implementations • 24 May 2023 • Feifei Shao, Yawei Luo, Lei Chen, Ping Liu, Yi Yang, Jun Xiao
In this paper, we focus on an under-explored issue of biased activation in prior weakly-supervised object localization methods based on Class Activation Mapping (CAM).
no code implementations • 25 Apr 2023 • Jiacheng Wang, Ping Liu, Jingen Liu, Wei Xu
To address these limitations, we propose a Text-guided Eyeglasses Manipulation method that allows for control of the eyeglasses shape and style based on a binary mask and text, respectively.
no code implementations • 24 Apr 2023 • Yaxin Shi, Xiaowei Zhou, Ping Liu, Ivor W. Tsang
Furthermore, we propose the use of transition consistency, defined on the transition variable, to enable regularization of consistency on unobserved translations, which is omitted in previous works.
no code implementations • 18 Apr 2023 • Gabriel Tjio, Ping Liu, Chee-Keong Kwoh, Joey Tianyi Zhou
Therefore, we address the problem of domain generalization for semantic segmentation tasks to reduce the need to acquire and label additional data.
no code implementations • 2 Feb 2023 • Ping Liu, Habib Ammari
We show that the resolution of multi-illumination imaging is approximately determined by the new imaging kernel from our operator theory and better resolution can be realized by sparsity-promoting techniques in practice but only for resolving very sparse sources.
no code implementations • 1 Dec 2022 • Ping Liu, Habib Ammari
In this paper, we analyze the capacity of super-resolution of one-dimensional positive sources.
no code implementations • 28 Nov 2022 • Ping Liu, Habib Ammari
The first contribution in this paper is two location-amplitude identities characterizing the relations between source locations and amplitudes in the super-resolution problem.
no code implementations • 24 Nov 2022 • Ping Liu, Yanchen He, Habib Ammari
The superresolving capacity for number and location recoveries in the super-resolution of positive sources is analyzed in this work.
1 code implementation • 29 Sep 2022 • Ping Liu, Xin Yu, Joey Tianyi Zhou
In this work, we first introduce a meta knowledge representation method that extracts meta knowledge from distributed clients.
no code implementations • 14 May 2022 • Ping Liu, Habib Ammari
The stability result exhibits the optimal performance of sparsity promoting in solving such problems; (iii) Our techniques pave the way for improving the estimate for resolution limits in higher-dimensional super-resolutions to nearly optimal; (iv) Inspired by these new techniques, we propose a new coordinate-combination-based model order detection algorithm for two-dimensional DOA estimation and theoretically demonstrate its optimal performance, and (v) we also propose a new coordinate-combination-based MUSIC algorithm for super-resolving sources in two-dimensional DOA estimation.
no code implementations • 8 Apr 2022 • Ping Liu, Habib Ammari
This result is derived by an observation that the inherent cut-off frequency for the velocity recovery can be viewed as the total observation time multiplies the cut-off frequency of the imaging system, which may lead to a better resolution limit as compared to the one for each diffraction-limited frame.
no code implementations • 1 Apr 2022 • Ping Liu, Hai Zhang
We consider the problem of resolving closely spaced point sources in one dimension from their Fourier data in a bounded domain.
no code implementations • 25 Feb 2022 • Feifei Shao, Yawei Luo, Ping Liu, Jie Chen, Yi Yang, Yulei Lu, Jun Xiao
To deploy SSDR-AL in a more practical scenario, we design a noise-aware iterative labeling strategy to confront the "noisy annotation" problem introduced by the previous "dominant labeling" strategy in superpoints.
no code implementations • 22 Feb 2022 • Ping Liu, Sanghyeon Yu, Ola Sabet, Lucas Pelkmans, Habib Ammari
By this estimation, we reveal the dependence of the resolution on the cut-off frequency of the imaging system, the SNR, the sparsity of point scatterers, and the incoherence of illumination patterns.
no code implementations • 29 Dec 2021 • Zhengqing Pan, Ruiqian Li, Tian Gao, Zi Wang, Ping Liu, Siyuan Shen, Tao Wu, Jingyi Yu, Shiying Li
There has been an increasing interest in deploying non-line-of-sight (NLOS) imaging systems for recovering objects behind an obstacle.
no code implementations • 28 Nov 2021 • Yang Peng, Ping Liu, Yawei Luo, Pan Zhou, Zichuan Xu, Jingen Liu
Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value.
Domain Adaptive Person Re-Identification
Person Re-Identification
no code implementations • 3 Aug 2021 • Bingwen Hu, Ping Liu, Zhedong Zheng, Mingwu Ren
Third, a Try-on Synthesis Module (TSM) combines the coarse result and the warped clothes to generate the final virtual try-on image, preserving details of the desired clothes and under the desired pose.
no code implementations • 2 Aug 2021 • Yang Zhang, Xin Yu, Xiaobo Lu, Ping Liu
Specifically, we design a novel cross-modal transformer module for facial priors estimation, in which an input face and its landmark features are formulated as queries and keys, respectively.
no code implementations • 20 Jun 2021 • Ping Liu, Yuewei Lin, Yang He, Yunchao Wei, Liangli Zhen, Joey Tianyi Zhou, Rick Siow Mong Goh, Jingen Liu
In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection.
1 code implementation • 8 Jun 2021 • Gabriel Tjio, Ping Liu, Joey Tianyi Zhou, Rick Siow Mong Goh
In this work, we propose an adversarial semantic hallucination approach (ASH), which combines a class-conditioned hallucination module and a semantic segmentation module.
no code implementations • 22 Mar 2021 • Ping Liu, Hai Zhang
Our results indicate that there exists a phase transition phenomenon regarding to the super-resolution factor and the signal-to-noise ratio in each of the two recovery problems.
no code implementations • 9 Mar 2021 • Yaxin Shi, Xiaowei Zhou, Ping Liu, Ivor Tsang
To benefit the generalization ability of the translation model, we propose transition encoding to facilitate explicit regularization of these two {kinds} of consistencies on unseen transitions.
1 code implementation • 2 Jan 2021 • Siyuan Shen, Zi Wang, Ping Liu, Zhengqing Pan, Ruiqian Li, Tian Gao, Shiying Li, Jingyi Yu
We present a neural modeling framework for Non-Line-of-Sight (NLOS) imaging.
no code implementations • 26 Aug 2020 • Ping Liu, Yuewei Lin, Zibo Meng, Lu Lu, Weihong Deng, Joey Tianyi Zhou, Yi Yang
In this paper, we propose a simple yet effective approach, named Point Adversarial Self Mining (PASM), to improve the recognition accuracy in facial expression recognition.
no code implementations • 18 May 2020 • Ping Liu, Yunchao Wei, Zibo Meng, Weihong Deng, Joey Tianyi Zhou, Yi Yang
However, the performance of the current state-of-the-art facial expression recognition (FER) approaches is directly related to the labeled data for training.
no code implementations • 7 May 2020 • Chenyou Fan, Ping Liu
This work studies training generative adversarial networks under the federated learning setting.
1 code implementation • NeurIPS 2020 • Yawei Luo, Ping Liu, Tao Guan, Junqing Yu, Yi Yang
We aim at the problem named One-Shot Unsupervised Domain Adaptation.
no code implementations • 26 Mar 2020 • Qilei Chen, Ping Liu, Jing Ni, Yu Cao, Benyuan Liu, Honggang Zhang
The first one is that our dataset is not fully labeled, i. e., only a subset of all lesion instances are marked.
no code implementations • 3 Mar 2020 • Ziling Wu, Ping Liu, Zheng Hu, Bocheng Li, Jun Wang
Our methods can significantly reduce the cost of development and maintenance of anomaly detection.
no code implementations • 9 Feb 2020 • Yang Zhang, Ivor W. Tsang, Jun Li, Ping Liu, Xiaobo Lu, Xin Yu
The coarse-level FHnet generates a frontal coarse HR face and then the fine-level FHnet makes use of the facial component appearance prior, i. e., fine-grained facial components, to attain a frontal HR face image with authentic details.
1 code implementation • ICCV 2019 • Zongxin Yang, Jian Dong, Ping Liu, Yi Yang, Shuicheng Yan
The second challenge is how to maintain high quality in generated results, especially for multi-step generations in which generated regions are spatially far away from the initial input.
1 code implementation • 16 Sep 2019 • Bingwen Hu, Zhedong Zheng, Ping Liu, Wankou Yang, Mingwu Ren
Given two facial images with and without eyeglasses, the proposed model learns to swap the eye area in two faces.
no code implementations • 6 Jun 2019 • Jie Cai, Zibo Meng, Ahmed Shehab Khan, Zhiyuan Li, James O'Reilly, Shizhong Han, Ping Liu, Min Chen, Yan Tong
In this paper, we proposed two strategies to fuse information extracted from different modalities, i. e., audio and visual.
no code implementations • SEMEVAL 2019 • Ping Liu, Wen Li, Liang Zou
Transfer learning and domain adaptive learning have been applied to various fields including computer vision (e. g., image recognition) and natural language processing (e. g., text classification).
no code implementations • 8 Apr 2019 • Yang He, Ping Liu, Linchao Zhu, Yi Yang
In addition, when evaluating the filter importance, only the magnitude information of the filters is considered.
no code implementations • ICCV 2019 • Yawei Luo, Ping Liu, Tao Guan, Junqing Yu, Yi Yang
For unsupervised domain adaptation problems, the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image classification, but usually fails in semantic segmentation tasks in which the latent representations are overcomplex.
no code implementations • 23 Jan 2019 • Guanghan Ning, Ping Liu, Xiaochuan Fan, Chi Zhang
Both the tasks of multi-person human pose estimation and pose tracking in videos are quite challenging.
3 code implementations • CVPR 2019 • Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, Yi Yang
In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small.
1 code implementation • 26 Sep 2018 • Yawei Luo, Tao Guan, Junqing Yu, Ping Liu, Yi Yang
To capitalize on the information from unlabeled nodes to boost the training for GCN, we propose a novel framework named Self-Ensembling GCN (SEGCN), which marries GCN with Mean Teacher - another powerful model in semi-supervised learning.
Ranked #4 on
Node Classification
on Cora: fixed 20 node per class
1 code implementation • 18 Apr 2018 • Ping Liu, Joshua Guberman, Libby Hemphill, Aron Culotta
Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities.
no code implementations • 14 Mar 2018 • Zhang Li, Zheyu Hu, Jiaolong Xu, Tao Tan, Hui Chen, Zhi Duan, Ping Liu, Jun Tang, Guoping Cai, Quchang Ouyang, Yuling Tang, Geert Litjens, Qiang Li
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome.
no code implementations • 10 Feb 2018 • Tao Tan, Zhang Li, Haixia Liu, Ping Liu, Wenfang Tang, Hui Li, Yue Sun, Yusheng Yan, Keyu Li, Tao Xu, Shanshan Wan, Ke Lou, Jun Xu, Huiming Ying, Quchang Ouyang, Yuling Tang, Zheyu Hu, Qiang Li
To help doctors to be more selective on biopsies and provide a second opinion on diagnosis, in this work, we propose a computer-aided diagnosis (CAD) system for lung diseases including cancers and tuberculosis (TB).
no code implementations • 29 Jun 2017 • Zibo Meng, Shizhong Han, Ping Liu, Yan Tong
Instead of solely improving visual observations, this paper presents a novel audiovisual fusion framework, which makes the best use of visual and acoustic cues in recognizing speech-related facial AUs.
no code implementations • CVPR 2014 • Ping Liu, Shizhong Han, Zibo Meng, Yan Tong
A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction.