Search Results for author: Ke Yang

Found 15 papers, 3 papers with code

Fairness in Ranking: A Survey

no code implementations25 Mar 2021 Meike Zehlike, Ke Yang, Julia Stoyanovich

In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and recommender systems communities.

Fairness Information Retrieval +1

Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction

no code implementations5 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.

Anomaly Detection

Two-dimensional ferromagnetic semiconductor VBr3 with tunable anisotropy

no code implementations20 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

Causal intersectionality for fair ranking

2 code implementations15 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.

Causal Inference Fairness

Is the four-dimensional novel EGB theory equivalent to its regularized counterpart in a cylindrically symmetric spacetime?

no code implementations14 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

Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways

1 code implementation18 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.

Autonomous Driving Scene Understanding +1

Towards Precise End-to-end Weakly Supervised Object Detection Network

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.

Multiple Instance Learning Weakly Supervised Object Detection

Balanced Ranking with Diversity Constraints

no code implementations4 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.

Fairness

IF-TTN: Information Fused Temporal Transformation Network for Video Action Recognition

no code implementations26 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.

Action Recognition Optical Flow Estimation

Exploring Frame Segmentation Networks for Temporal Action Localization

no code implementations14 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.

Temporal Action Localization

Exploring Temporal Preservation Networks for Precise Temporal Action Localization

no code implementations10 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.

Temporal Action Localization Temporal Localization

Weakly supervised object detection using pseudo-strong labels

no code implementations16 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.

Weakly Supervised Object Detection

Relative distance features for gait recognition with Kinect

no code implementations18 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.

Gait Recognition

Large Scale Distributed Deep Networks

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.

Object Recognition Speech Recognition

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