Search Results for author: Li Cheng

Found 32 papers, 7 papers with code

Automated Generation of Accurate \& Fluent Medical X-ray Reports

no code implementations27 Aug 2021 Hoang T. N. Nguyen, Dong Nie, Taivanbat Badamdorj, Yujie Liu, Yingying Zhu, Jason Truong, Li Cheng

Our paper focuses on automating the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists.

EventHPE: Event-based 3D Human Pose and Shape Estimation

no code implementations15 Aug 2021 Shihao Zou, Chuan Guo, Xinxin Zuo, Sen Wang, Pengyu Wang, Xiaoqin Hu, Shoushun Chen, Minglun Gong, Li Cheng

Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals.

Optical Flow Estimation

Human Pose and Shape Estimation from Single Polarization Images

no code implementations15 Aug 2021 Shihao Zou, Xinxin Zuo, Sen Wang, Yiming Qian, Chuan Guo, Wei Ji, Jingjing Li, Minglun Gong, Li Cheng

This paper focuses on a new problem of estimating human pose and shape from single polarization images.

Object Wake-up: 3-D Object Reconstruction, Animation, and in-situ Rendering from a Single Image

no code implementations5 Aug 2021 Xinxin Zuo, Ji Yang, Sen Wang, Zhenbo Yu, Xinyu Li, Bingbing Ni, Minglun Gong, Li Cheng

The pipeline of our approach starts by reconstructing and refining a 3-D mesh representation of the object of interest from an input image; its control joints are predicted by exploiting the semantic part segmentation information; the obtained object 3-D mesh is then rigged \& animated by non-rigid deformation, and rendered to perform in-situ motions in its original image space.

Object Reconstruction

Unsupervised 3D Human Mesh Recovery from Noisy Point Clouds

1 code implementation15 Jul 2021 Xinxin Zuo, Sen Wang, Minglun Gong, Li Cheng

Relying on large amount of dataset with ground-truth annotations, recent learning-based approaches predict correspondences for every vertice on the point cloud; Chamfer distance is usually used to minimize the distance between a deformed template model and the input point cloud.

CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture Search

no code implementations2 Jul 2021 Seyed Mojtaba Marvasti-Zadeh, Javad Khaghani, Li Cheng, Hossein Ghanei-Yakhdan, Shohreh Kasaei

A strong visual object tracker nowadays relies on its well-crafted modules, which typically consist of manually-designed network architectures to deliver high-quality tracking results.

Neural Architecture Search Semantic Segmentation +1

Calibrated RGB-D Salient Object Detection

1 code implementation CVPR 2021 Wei Ji, Jingjing Li, Shuang Yu, Miao Zhang, Yongri Piao, Shunyu Yao, Qi Bi, Kai Ma, Yefeng Zheng, Huchuan Lu, Li Cheng

Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD).

RGB-D Salient Object Detection Salient Object Detection

Learning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling

1 code implementation CVPR 2021 Wei Ji, Shuang Yu, Junde Wu, Kai Ma, Cheng Bian, Qi Bi, Jingjing Li, Hanruo Liu, Li Cheng, Yefeng Zheng

To our knowledge, our work is the first in producing calibrated predictions under different expertise levels for medical image segmentation.

Medical Image Segmentation

Reconstruct high-resolution multi-focal plane images from a single 2D wide field image

no code implementations21 Sep 2020 Jiabo Ma, Sibo Liu, Shenghua Cheng, Xiuli Liu, Li Cheng, Shaoqun Zeng

High-resolution 3D medical images are important for analysis and diagnosis, but axial scanning to acquire them is very time-consuming.

Super-Resolution

Action2Motion: Conditioned Generation of 3D Human Motions

1 code implementation30 Jul 2020 Chuan Guo, Xinxin Zuo, Sen Wang, Shihao Zou, Qingyao Sun, Annan Deng, Minglun Gong, Li Cheng

Action recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category.

Action Generation

3D Human Shape Reconstruction from a Polarization Image

no code implementations ECCV 2020 Shihao Zou, Xinxin Zuo, Yiming Qian, Sen Wang, Chi Xu, Minglun Gong, Li Cheng

Inspired by the recent advances in human shape estimation from single color images, in this paper, we attempt at estimating human body shapes by leveraging the geometric cues from single polarization images.

SparseFusion: Dynamic Human Avatar Modeling from Sparse RGBD Images

no code implementations5 Jun 2020 Xinxin Zuo, Sen Wang, Jiangbin Zheng, Weiwei Yu, Minglun Gong, Ruigang Yang, Li Cheng

First, based on a generative human template, for every two frames having sufficient overlap, an initial pairwise alignment is performed; It is followed by a global non-rigid registration procedure, in which partial results from RGBD frames are collected into a unified 3D shape, under the guidance of correspondences from the pairwise alignment; Finally, the texture map of the reconstructed human model is optimized to deliver a clear and spatially consistent texture.

COMET: Context-Aware IoU-Guided Network for Small Object Tracking

no code implementations4 Jun 2020 Seyed Mojtaba Marvasti-Zadeh, Javad Khaghani, Hossein Ghanei-Yakhdan, Shohreh Kasaei, Li Cheng

To address this problem, we introduce a context-aware IoU-guided tracker (COMET) that exploits a multitask two-stream network and an offline reference proposal generation strategy.

Object Tracking

Polarization Human Shape and Pose Dataset

no code implementations30 Apr 2020 Shihao Zou, Xinxin Zuo, Yiming Qian, Sen Wang, Chuan Guo, Chi Xu, Minglun Gong, Li Cheng

Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing detailed surface normal of the objects of interest.

Stabilizing Training of Generative Adversarial Nets via Langevin Stein Variational Gradient Descent

no code implementations22 Apr 2020 Dong Wang, Xiaoqian Qin, Fengyi Song, Li Cheng

Generative adversarial networks (GANs), famous for the capability of learning complex underlying data distribution, are however known to be tricky in the training process, which would probably result in mode collapse or performance deterioration.

Variational Inference

Outlier Detection Ensemble with Embedded Feature Selection

no code implementations15 Jan 2020 Li Cheng, Yijie Wang, Xinwang Liu, Bin Li

Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance.

Feature Selection Outlier Detection

Deep Learning for Visual Tracking: A Comprehensive Survey

1 code implementation2 Dec 2019 Seyed Mojtaba Marvasti-Zadeh, Li Cheng, Hossein Ghanei-Yakhdan, Shohreh Kasaei

Second, popular visual tracking benchmarks and their respective properties are compared, and their evaluation metrics are summarized.

Visual Tracking

WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis

1 code implementation12 Nov 2019 Tianfu Li, Zhibin Zhao, Chuang Sun, Li Cheng, Xuefeng Chen, Ruqiang Yan, Robert X. Gao

In this paper, a novel wavelet driven deep neural network termed as WaveletKernelNet (WKN) is presented, where a continuous wavelet convolutional (CWConv) layer is designed to replace the first convolutional layer of the standard CNN.

Estimating Position Bias without Intrusive Interventions

no code implementations12 Dec 2018 Agarwal Aman, Zaitsev Ivan, Wang Xuanhui, Li Cheng, Najork Marc, Joachims Thorsten

Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal.

Learning-To-Rank

Offline Comparison of Ranking Functions using Randomized Data

no code implementations11 Oct 2018 Agarwal Aman, Wang Xuanhui, Li Cheng, Bendersky Michael, Najork Marc

In this paper, we study how to improve the data efficiency of IPS approaches in the offline comparison setting.

Too Far to See? Not Really! --- Pedestrian Detection with Scale-aware Localization Policy

no code implementations1 Sep 2017 Xiaowei Zhang, Li Cheng, Bo Li, Hai-Miao Hu

A major bottleneck of pedestrian detection lies on the sharp performance deterioration in the presence of small-size pedestrians that are relatively far from the camera.

Pedestrian Detection Region Proposal

Synthesizing Filamentary Structured Images with GANs

1 code implementation7 Jun 2017 He Zhao, Huiqi Li, Li Cheng

This paper aims at synthesizing filamentary structured images such as retinal fundus images and neuronal images, as follows: Given a ground-truth, to generate multiple realistic looking phantoms.

Style Transfer

Multivariate Regression with Gross Errors on Manifold-valued Data

no code implementations26 Mar 2017 Xiaowei Zhang, Xudong Shi, Yu Sun, Li Cheng

Our model first takes a correction step on the grossly corrupted responses via geodesic curves on the manifold, and then performs multivariate linear regression on the corrected data.

Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications

no code implementations11 Jan 2017 Xiaowei Zhang, Chi Xu, Yu Zhang, Tingshao Zhu, Li Cheng

The implementation of our approach and comparison methods as well as the involved datasets are made publicly available in support of the open-source and reproducible research initiatives.

Hand Pose Estimation

An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition

no code implementations4 Jan 2017 Li Liu, Yongzhong Yang, Lakshmi Narasimhan Govindarajan, Shu Wang, Bin Hu, Li Cheng, David S. Rosenblum

We propose in this paper an atomic action-based Bayesian model that constructs Allen's interval relation networks to characterize complex activities with structural varieties in a probabilistic generative way: By introducing latent variables from the Chinese restaurant process, our approach is able to capture all possible styles of a particular complex activity as a unique set of distributions over atomic actions and relations.

Activity Recognition

Learning to Search on Manifolds for 3D Pose Estimation of Articulated Objects

no code implementations2 Dec 2016 Yu Zhang, Chi Xu, Li Cheng

This paper focuses on the challenging problem of 3D pose estimation of a diverse spectrum of articulated objects from single depth images.

3D Pose Estimation Structured Prediction

Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups

no code implementations13 Sep 2016 Chi Xu, Lakshmi Narasimhan Govindarajan, Yu Zhang, Li Cheng

Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately.

Action Recognition Pose Estimation

Hand Action Detection from Ego-centric Depth Sequences with Error-correcting Hough Transform

no code implementations7 Jun 2016 Chi Xu, Lakshmi Narasimhan Govindarajan, Li Cheng

Detecting hand actions from ego-centric depth sequences is a practically challenging problem, owing mostly to the complex and dexterous nature of hand articulations as well as non-stationary camera motion.

Action Detection Action Recognition

Learning to Boost Filamentary Structure Segmentation

no code implementations ICCV 2015 Lin Gu, Li Cheng

Step one of our approach centers on a data-driven latent classification tree model to detect the filamentary fragments.

Mouse Pose Estimation From Depth Images

no code implementations24 Nov 2015 Ashwin Nanjappa, Li Cheng, Wei Gao, Chi Xu, Adam Claridge-Chang, Zoe Bichler

We focus on the challenging problem of efficient mouse 3D pose estimation based on static images, and especially single depth images.

3D Pose Estimation

Transduction on Directed Graphs via Absorbing Random Walks

no code implementations19 Feb 2014 Jaydeep De, Xiaowei Zhang, Li Cheng

In this paper we consider the problem of graph-based transductive classification, and we are particularly interested in the directed graph scenario which is a natural form for many real world applications.

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