1 code implementation • 21 Feb 2023 • Weizhou Shen, Xiaojun Quan, Ke Yang
To model the dependencies between utterances in multi-party conversations, we propose a simple and generic framework based on the dependency parsing results of utterances.
no code implementations • 29 Jan 2023 • Peng Qiao, Sidun Liu, Tao Sun, Ke Yang, Yong Dou
It provides a promising way to introduce the Transformer in low-level vision tasks.
1 code implementation • 10 Nov 2022 • Ke Yang, Charles Yu, Yi Fung, Manling Li, Heng Ji
Despite this, relatively few efforts have been made to debias PLMs by prompt tuning with continuous prompts compared to its discrete counterpart.
no code implementations • 8 Nov 2022 • Jincheng Dai, Sixian Wang, Ke Yang, Kailin Tan, Xiaoqi Qin, Zhongwei Si, Kai Niu, Ping Zhang
This new system is named adaptive semantic communication (ASC).
1 code implementation • 2 Nov 2022 • Ke Yang, Sixian Wang, Jincheng Dai, Kailin Tan, Kai Niu, Ping Zhang
In this paper, we aim to redesign the vision Transformer (ViT) as a new backbone to realize semantic image transmission, termed wireless image transmission transformer (WITT).
no code implementations • 18 Aug 2022 • Heming Yang, Ke Yang, Erhan Zhang
What's more, to our best knowledge, the proposed BCM model is the first work on using NLP to solve endorsement issues, so it can provide some novel research ideas and methodologies for the following works.
1 code implementation • Findings (ACL) 2022 • Haochen Tan, Wei Shao, Han Wu, Ke Yang, Linqi Song
Contrastive learning has shown great potential in unsupervised sentence embedding tasks, e. g., SimCSE.
no code implementations • 25 Jan 2022 • Sixian Wang, Ke Yang, Jincheng Dai, Kai Niu
In particular, we consider a pair of images captured by two cameras with probably overlapping fields of view transmitted over wireless channels and reconstructed in the center node.
no code implementations • 25 Mar 2021 • Meike Zehlike, Ke Yang, Julia Stoyanovich
In this survey, we describe four classification frameworks for fairness-enhancing interventions, along which we relate the technical methods surveyed in this paper, discuss evaluation datasets, and present technical work on fairness in score-based ranking.
no code implementations • 16 Mar 2021 • Hongjie He, Ke Yang, Yuwei Cai, Zijian Jiang, Qiutong Yu, Kun Zhao, JunBo Wang, Sarah Narges Fatholahi, Yan Liu, Hasti Andon Petrosians, Bingxu Hu, Liyuan Qing, Zhehan Zhang, Hongzhang Xu, Siyu Li, Kyle Gao, Linlin Xu, Jonathan Li
Building rooftop data are of importance in several urban applications and in natural disaster management.
no code implementations • 5 Nov 2020 • Xuanzhao Wang, Zhengping Che, Bo Jiang, Ning Xiao, Ke Yang, Jian Tang, Jieping Ye, Jingyu Wang, Qi Qi
In this paper, we propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design which is more in line with the characteristics of surveillance videos.
no code implementations • 20 Aug 2020 • Lu Liu, Ke Yang, Guangyu Wang, Hua Wu
Two-dimensional (2D) ferromagnets (FMs) have attracted widespread attention due to their prospects in spintronic applications.
Materials Science Strongly Correlated Electrons
2 code implementations • 15 Jun 2020 • Ke Yang, Joshua R. Loftus, Julia Stoyanovich
In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings.
no code implementations • 14 Jun 2020 • Zi-Chao Lin, Ke Yang, Shao-Wen Wei, Yong-Qiang Wang, Yu-Xiao Liu
Thus it is expected that the novel four-dimensional EGB theory is equivalent to its regularized version.
General Relativity and Quantum Cosmology High Energy Physics - Theory
1 code implementation • 18 Mar 2020 • Weikai Tan, Nannan Qin, Lingfei Ma, Ying Li, Jing Du, Guorong Cai, Ke Yang, Jonathan Li
Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping.
1 code implementation • ICCV 2019 • Ke Yang, Dongsheng Li, Yong Dou
It is challenging for weakly supervised object detection network to precisely predict the positions of the objects, since there are no instance-level category annotations.
no code implementations • 4 Jun 2019 • Ke Yang, Vasilis Gkatzelis, Julia Stoyanovich
Many set selection and ranking algorithms have recently been enhanced with diversity constraints that aim to explicitly increase representation of historically disadvantaged populations, or to improve the overall representativeness of the selected set.
no code implementations • 26 Feb 2019 • Ke Yang, Peng Qiao, Dongsheng Li, Yong Dou
Focusing on discriminate spatiotemporal feature learning, we propose Information Fused Temporal Transformation Network (IF-TTN) for action recognition on top of popular Temporal Segment Network (TSN) framework.
no code implementations • 14 Feb 2019 • Ke Yang, Xiaolong Shen, Peng Qiao, Shijie Li, Dongsheng Li, Yong Dou
The proposed FSN can make dense predictions at frame-level for a video clip using both spatial and temporal context information.
no code implementations • 10 Aug 2017 • Ke Yang, Peng Qiao, Dongsheng Li, Shaohe Lv, Yong Dou
A newly proposed work exploits Convolutional-Deconvolutional-Convolutional (CDC) filters to upsample the predictions of 3D ConvNets, making it possible to perform per-frame action predictions and achieving promising performance in terms of temporal action localization.
no code implementations • 16 Jul 2016 • Ke Yang, Dongsheng Li, Yong Dou, Shaohe Lv, Qiang Wang
Object detection is an import task of computer vision. A variety of methods have been proposed, but methods using the weak labels still do not have a satisfactory result. In this paper, we propose a new framework that using the weakly supervised method's output as the pseudo-strong labels to train a strongly supervised model. One weakly supervised method is treated as black-box to generate class-specific bounding boxes on train dataset. A de-noise method is then applied to the noisy bounding boxes. Then the de-noised pseudo-strong labels are used to train a strongly object detection network. The whole framework is still weakly supervised because the entire process only uses the image-level labels. The experiment results on PASCAL VOC 2007 prove the validity of our framework, and we get result 43. 4% on mean average precision compared to 39. 5% of the previous best result and 34. 5% of the initial method, respectively. And this frame work is simple and distinct, and is promising to be applied to other method easily.
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.
no code implementations • NeurIPS 2012 • Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc'Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, Quoc V. Le, Andrew Y. Ng
Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance.