Search Results for author: Yun Liu

Found 75 papers, 26 papers with code

Towards Efficient Single Image Dehazing and Desnowing

no code implementations19 Apr 2022 Tian Ye, Sixiang Chen, Yun Liu, ErKang Chen, Yuche Li

A single expert network efficiently addresses specific degradation in nasty winter scenes relying on the compact architecture and three novel components.

Image Dehazing Image Restoration +1

Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule Diagnosis

no code implementations8 Apr 2022 Chenglong Wang, Yun Liu, Fen Wang, Chengxiu Zhang, Yida Wang, Mei Yuan, Guang Yang

However, detection and accurate diagnosis of pulmonary nodules depend heavily on the experiences of radiologists and can be a heavy workload for them.

Coarse-to-Fine Feature Mining for Video Semantic Segmentation

1 code implementation CVPR 2022 Guolei Sun, Yun Liu, Henghui Ding, Thomas Probst, Luc van Gool

To address this problem, we propose a Coarse-to-Fine Feature Mining (CFFM) technique to learn a unified presentation of static contexts and motional contexts.

Semantic Segmentation Video Semantic Segmentation

Joint Noise Reduction and Listening Enhancement for Full-End Speech Enhancement

no code implementations22 Mar 2022 Haoyu Li, Yun Liu, Junichi Yamagishi

Speech enhancement (SE) methods mainly focus on recovering clean speech from noisy input.

Speech Enhancement

Underwater Light Field Retention : Neural Rendering for Underwater Imaging

1 code implementation21 Mar 2022 Tian Ye, Sixiang Chen, Yun Liu, Yi Ye, ErKang Chen, Yuche Li

To this end, we propose a neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural Rendering).

Image Enhancement Image Generation +1

Mutual Learning for Domain Adaptation: Self-distillation Image Dehazing Network with Sample-cycle

no code implementations17 Mar 2022 Tian Ye, Yun Liu, Yunchen Zhang, Sixiang Chen, ErKang Chen

Specifically, we first devise two siamese networks: a teacher network in the synthetic domain and a student network in the real domain, and then optimize them in a mutual learning manner by leveraging EMA and joint loss.

Domain Adaptation Image Dehazing

Zero Pixel Directional Boundary by Vector Transform

no code implementations ICLR 2022 Edoardo Mello Rella, Ajad Chhatkuli, Yun Liu, Ender Konukoglu, Luc van Gool

One of the key problems in boundary detection is the label representation, which typically leads to class imbalance and, as a consequence, to thick boundaries that require non-differential post-processing steps to be thinned.

Boundary Detection Computer Vision

LEVEN: A Large-Scale Chinese Legal Event Detection Dataset

1 code implementation Findings (ACL) 2022 Feng Yao, Chaojun Xiao, Xiaozhi Wang, Zhiyuan Liu, Lei Hou, Cunchao Tu, Juanzi Li, Yun Liu, Weixing Shen, Maosong Sun

However, existing Legal Event Detection (LED) datasets only concern incomprehensive event types and have limited annotated data, which restricts the development of LED methods and their downstream applications.

Event Detection

Asymptotic affirmative actions: The top trading cycles and the Boston mechanism case

no code implementations8 Feb 2022 Di Feng, Yun Liu

This paper analyzes the asymptotic performance of two popular affirmative action policies, majority quota and minority reserve, under the top trading cycles mechanism (TTCM) and the Boston mechanism (BM).

On the Equivalence of Two Competing Affirmative Actions in School Choice

no code implementations28 Dec 2021 Yun Liu

This note analyzes the outcome equivalence conditions of two popular affirmative action policies, majority quota and minority reserve, under the student optimal stable mechanism.

Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net

no code implementations17 Aug 2021 Yu Qiu, Yun Liu, Le Zhang, Jing Xu

The asymmetric bilateral encoder has a transformer path and a lightweight CNN path, where the two paths communicate at each encoder stage to learn complementary global contexts and local spatial details, respectively.

Computer Vision object-detection +2

Boosting Few-shot Semantic Segmentation with Transformers

no code implementations4 Aug 2021 Guolei Sun, Yun Liu, Jingyun Liang, Luc van Gool

Due to the fact that fully supervised semantic segmentation methods require sufficient fully-labeled data to work well and can not generalize to unseen classes, few-shot segmentation has attracted lots of research attention.

Few-Shot Semantic Segmentation Semantic Segmentation

Vision Transformers with Hierarchical Attention

1 code implementation6 Jun 2021 Yun Liu, Yu-Huan Wu, Guolei Sun, Le Zhang, Ajad Chhatkuli, Luc van Gool

This paper tackles the low-efficiency flaw of the vision transformer caused by the high computational/space complexity in Multi-Head Self-Attention (MHSA).

Image Classification Instance Segmentation +4

Boosting Crowd Counting with Transformers

no code implementations23 May 2021 Guolei Sun, Yun Liu, Thomas Probst, Danda Pani Paudel, Nikola Popovic, Luc van Gool

This indicates that global scene context is essential, despite the seemingly bottom-up nature of the problem.

Crowd Counting

Coarse-grained decomposition and fine-grained interaction for multi-hop question answering

no code implementations15 Jan 2021 Xing Cao, Yun Liu

Recent advances regarding question answering and reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text, requiring only single-hop reasoning.

Multi-hop Question Answering Question Answering +1

MobileSal: Extremely Efficient RGB-D Salient Object Detection

1 code implementation24 Dec 2020 Yu-Huan Wu, Yun Liu, Jun Xu, Jia-Wang Bian, Yu-Chao Gu, Ming-Ming Cheng

Therefore, we propose an implicit depth restoration (IDR) technique to strengthen the mobile networks' feature representation capability for RGB-D SOD.

object-detection RGB-D Salient Object Detection +1

EDN: Salient Object Detection via Extremely-Downsampled Network

1 code implementation24 Dec 2020 Yu-Huan Wu, Yun Liu, Le Zhang, Ming-Ming Cheng, Bo Ren

In this paper, we tap into this gap and show that enhancing high- level features is essential for SOD as well.

object-detection Object Detection +2

Detecting hidden signs of diabetes in external eye photographs

no code implementations23 Nov 2020 Boris Babenko, Akinori Mitani, Ilana Traynis, Naho Kitade, Preeti Singh, April Maa, Jorge Cuadros, Greg S. Corrado, Lily Peng, Dale R. Webster, Avinash Varadarajan, Naama Hammel, Yun Liu

In validation set A (n=27, 415 patients, all undilated), the DLS detected poor blood glucose control (HbA1c > 9%) with an area under receiver operating characteristic curve (AUC) of 70. 2; moderate-or-worse DR with an AUC of 75. 3; diabetic macular edema with an AUC of 78. 0; and vision-threatening DR with an AUC of 79. 4.

Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation

1 code implementation10 Sep 2020 Yun Liu, Yu-Huan Wu, Pei-Song Wen, Yu-Jun Shi, Yu Qiu, Ming-Ming Cheng

For each proposal, this MIL framework can simultaneously compute probability distributions and category-aware semantic features, with which we can formulate a large undirected graph.

Image-level Supervised Instance Segmentation Multiple Instance Learning +2

Generalized Zero-Shot Learning via VAE-Conditioned Generative Flow

1 code implementation1 Sep 2020 Yu-Chao Gu, Le Zhang, Yun Liu, Shao-Ping Lu, Ming-Ming Cheng

Recent generative methods formulate GZSL as a missing data problem, which mainly adopts GANs or VAEs to generate visual features for unseen classes.

Generalized Zero-Shot Learning

Regularized Densely-connected Pyramid Network for Salient Instance Segmentation

1 code implementation28 Aug 2020 Yu-Huan Wu, Yun Liu, Le Zhang, Wang Gao, Ming-Ming Cheng

Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels.

Instance Segmentation object-detection +3

Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform

2 code implementations20 Aug 2020 Shijie Li, Xieyuanli Chen, Yun Liu, Dengxin Dai, Cyrill Stachniss, Juergen Gall

Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources.

Autonomous Vehicles Real-Time 3D Semantic Segmentation +1

Rethinking 3D LiDAR Point Cloud Segmentation

1 code implementation10 Aug 2020 Shijie Li, Yun Liu, Juergen Gall

Many point-based semantic segmentation methods have been designed for indoor scenarios, but they struggle if they are applied to point clouds that are captured by a LiDAR sensor in an outdoor environment.

Autonomous Driving Point Cloud Segmentation +1

DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement

7 code implementations Interspeech 2020 Yanxin Hu, Yun Liu, Shubo Lv, Mengtao Xing, Shimin Zhang, Yihui Fu, Jian Wu, Bihong Zhang, Lei Xie

Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality.

Speech Enhancement Audio and Speech Processing Sound

Efficacious symmetry-adapted atomic displacement method for lattice dynamical studies

1 code implementation14 Jul 2020 Chee Kwan Gan, Yun Liu, Tze Chien Sum, Kedar Hippalgaonkar

Small displacement methods have been successfully used to calculate the lattice dynamical properties of crystals.

Materials Science

Deep Learning for Strong Lensing Search: Tests of the Convolutional Neural Networks and New Candidates from KiDS DR3

no code implementations1 Jul 2020 Zizhao He, Xinzhong Er, Qian Long, Dezi Liu, Xiangkun Liu, Ziwei Li, Yun Liu, Wenqaing Deng, Zuhui Fan

Using the latter training set, about 67\% of the aforementioned 48 candidates are also found, and there are 11 more new strong lensing candidates identified.

Cosmology and Nongalactic Astrophysics

MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation

1 code implementation16 Jun 2020 Shijie Li, Yazan Abu Farha, Yun Liu, Ming-Ming Cheng, Juergen Gall

Despite the capabilities of these approaches in capturing temporal dependencies, their predictions suffer from over-segmentation errors.

Action Segmentation

Rethinking Computer-Aided Tuberculosis Diagnosis

no code implementations CVPR 2020 Yun Liu, Yu-Huan Wu, Yunfeng Ban, Huifang Wang, Ming-Ming Cheng

Computer-aided tuberculosis diagnosis (CTD) is a promising choice for TB diagnosis due to the great successes of deep learning.

Image Classification

Sub-Band Knowledge Distillation Framework for Speech Enhancement

no code implementations29 May 2020 Xiang Hao, Shixue Wen, Xiangdong Su, Yun Liu, Guanglai Gao, Xiaofei Li

In single-channel speech enhancement, methods based on full-band spectral features have been widely studied.

Knowledge Distillation Speech Enhancement

Revealing Cluster Structures Based on Mixed Sampling Frequencies

no code implementations21 Apr 2020 Yeonwoo Rho, Yun Liu, Hie Joo Ahn

This paper proposes a new linearized mixed data sampling (MIDAS) model and develops a framework to infer clusters in a panel regression with mixed frequency data.

MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation

1 code implementation21 Apr 2020 Yu Qiu, Yun Liu, Shijie Li, Jing Xu

On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive.

COVID-19 Diagnosis Semantic Segmentation

Ordered or Orderless: A Revisit for Video based Person Re-Identification

no code implementations24 Dec 2019 Le Zhang, Zenglin Shi, Joey Tianyi Zhou, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Zeng Zeng, Chunhua Shen

Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation.

Video-Based Person Re-Identification

Deep learning-based survival prediction for multiple cancer types using histopathology images

no code implementations16 Dec 2019 Ellery Wulczyn, David F. Steiner, Zhaoyang Xu, Apaar Sadhwani, Hongwu Wang, Isabelle Flament, Craig H. Mermel, Po-Hsuan Cameron Chen, Yun Liu, Martin C. Stumpe

Our analysis demonstrates the potential for this approach to provide prognostic information in multiple cancer types, and even within specific pathologic stages.

Survival Prediction

AdaSample: Adaptive Sampling of Hard Positives for Descriptor Learning

no code implementations27 Nov 2019 Xin-Yu Zhang, Le Zhang, Zao-Yi Zheng, Yun Liu, Jia-Wang Bian, Ming-Ming Cheng

The effectiveness of the triplet loss heavily relies on the triplet selection, in which a common practice is to first sample intra-class patches (positives) from the dataset for batch construction and then mine in-batch negatives to form triplets.

Computer Vision Informativeness

Towards Disentangling Non-Robust and Robust Components in Performance Metric

no code implementations25 Sep 2019 Yujun Shi, Benben Liao, Guangyong Chen, Yun Liu, Ming-Ming Cheng, Jiashi Feng

Then, we show by experiments that DNNs under standard training rely heavily on optimizing the non-robust component in achieving decent performance.

Adversarial Robustness

A deep learning system for differential diagnosis of skin diseases

no code implementations11 Sep 2019 Yuan Liu, Ayush Jain, Clara Eng, David H. Way, Kang Lee, Peggy Bui, Kimberly Kanada, Guilherme de Oliveira Marinho, Jessica Gallegos, Sara Gabriele, Vishakha Gupta, Nalini Singh, Vivek Natarajan, Rainer Hofmann-Wellenhof, Greg S. Corrado, Lily H. Peng, Dale R. Webster, Dennis Ai, Susan Huang, Yun Liu, R. Carter Dunn, David Coz

In this paper, we developed a deep learning system (DLS) to provide a differential diagnosis of skin conditions for clinical cases (skin photographs and associated medical histories).

An Evaluation of Feature Matchers for Fundamental Matrix Estimation

no code implementations26 Aug 2019 Jia-Wang Bian, Yu-Huan Wu, Ji Zhao, Yun Liu, Le Zhang, Ming-Ming Cheng, Ian Reid

According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator.

Computer Vision

Robust Regression via Deep Negative Correlation Learning

no code implementations24 Aug 2019 Le Zhang, Zenglin Shi, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Joey Tianyi Zhou, Guoyan Zheng, Zeng Zeng

Nonlinear regression has been extensively employed in many computer vision problems (e. g., crowd counting, age estimation, affective computing).

Age Estimation Computer Vision +2

Scoot: A Perceptual Metric for Facial Sketches

1 code implementation ICCV 2019 Deng-Ping Fan, Shengchuan Zhang, Yu-Huan Wu, Yun Liu, Ming-Ming Cheng, Bo Ren, Paul L. Rosin, Rongrong Ji

In this paper, we design a perceptual metric, called Structure Co-Occurrence Texture (Scoot), which simultaneously considers the block-level spatial structure and co-occurrence texture statistics.

Face Sketch Synthesis SSIM

Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric

no code implementations6 Jun 2019 Yujun Shi, Benben Liao, Guangyong Chen, Yun Liu, Ming-Ming Cheng, Jiashi Feng

Despite many previous works studying the reason behind such adversarial behavior, the relationship between the generalization performance and adversarial behavior of DNNs is still little understood.

Adversarial Robustness

Detecting Anemia from Retinal Fundus Images

no code implementations12 Apr 2019 Akinori Mitani, Yun Liu, Abigail Huang, Greg S. Corrado, Lily Peng, Dale R. Webster, Naama Hammel, Avinash V. Varadarajan

Despite its high prevalence, anemia is often undetected due to the invasiveness and cost of screening and diagnostic tests.

DNA: Deeply-supervised Nonlinear Aggregation for Salient Object Detection

1 code implementation28 Mar 2019 Yun Liu, Ming-Ming Cheng, Xin-Yu Zhang, Guang-Yu Nie, Meng Wang

Recent progress on salient object detection mainly aims at exploiting how to effectively integrate multi-scale convolutional features in convolutional neural networks (CNNs).

object-detection RGB Salient Object Detection +2

Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality

no code implementations17 Feb 2019 Guang-Yu Nie, Yun Liu, Cong Wang, Yue Liu, Yongtian Wang

Three-dimensional (3-D) scene reconstruction is one of the key techniques in Augmented Reality (AR), which is related to the integration of image processing and display systems of complex information.

Computer Vision Stereo Matching +1

Similar Image Search for Histopathology: SMILY

no code implementations30 Jan 2019 Narayan Hegde, Jason D. Hipp, Yun Liu, Michael E. Buck, Emily Reif, Daniel Smilkov, Michael Terry, Carrie J. Cai, Mahul B. Amin, Craig H. Mermel, Phil Q. Nelson, Lily H. Peng, Greg S. Corrado, Martin C. Stumpe

SMILY may be a useful general-purpose tool in the pathologist's arsenal, to improve the efficiency of searching large archives of histopathology images, without the need to develop and implement specific tools for each application.

Image Retrieval

Salient Object Detection via High-to-Low Hierarchical Context Aggregation

no code implementations28 Dec 2018 Yun Liu, Yu Qiu, Le Zhang, Jia-Wang Bian, Guang-Yu Nie, Ming-Ming Cheng

In this paper, we observe that the contexts of a natural image can be well expressed by a high-to-low self-learning of side-output convolutional features.

object-detection RGB Salient Object Detection +3

Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration

no code implementations21 Nov 2018 Po-Hsuan Cameron Chen, Krishna Gadepalli, Robert MacDonald, Yun Liu, Kunal Nagpal, Timo Kohlberger, Jeffrey Dean, Greg S. Corrado, Jason D. Hipp, Martin C. Stumpe

We demonstrate the utility of ARM in the detection of lymph node metastases in breast cancer and the identification of prostate cancer with a latency that supports real-time workflows.

MatchBench: An Evaluation of Feature Matchers

no code implementations7 Aug 2018 Jia-Wang Bian, Ruihan Yang, Yun Liu, Le Zhang, Ming-Ming Cheng, Ian Reid, WenHai Wu

This leads to a critical absence in this field that there is no standard datasets and evaluation metrics to evaluate different feature matchers fairly.

Computer Vision

Learning Pixel-wise Labeling from the Internet without Human Interaction

no code implementations19 May 2018 Yun Liu, Yujun Shi, Jia-Wang Bian, Le Zhang, Ming-Ming Cheng, Jiashi Feng

Collecting sufficient annotated data is very expensive in many applications, especially for pixel-level prediction tasks such as semantic segmentation.

Computer Vision Semantic Segmentation

Semantic Edge Detection with Diverse Deep Supervision

1 code implementation9 Apr 2018 Yun Liu, Ming-Ming Cheng, Deng-Ping Fan, Le Zhang, JiaWang Bian, DaCheng Tao

Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition.

Edge Detection Object Proposal Generation +2

Image Matching: An Application-oriented Benchmark

no code implementations12 Sep 2017 Jia-Wang Bian, Le Zhang, Yun Liu, Wen-Yan Lin, Ming-Ming Cheng, Ian D. Reid

To this end, we present a uniform benchmark with novel evaluation metrics and a large-scale dataset for evaluating the overall performance of image matching methods.

Computer Vision

Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning

no code implementations31 Aug 2017 Ryan Poplin, Avinash V. Varadarajan, Katy Blumer, Yun Liu, Michael V. McConnell, Greg S. Corrado, Lily Peng, Dale R. Webster

Traditionally, medical discoveries are made by observing associations and then designing experiments to test these hypotheses.

Structure-measure: A New Way to Evaluate Foreground Maps

1 code implementation ICCV 2017 Deng-Ping Fan, Ming-Ming Cheng, Yun Liu, Tao Li, Ali Borji

Our new measure simultaneously evaluates region-aware and object-aware structural similarity between a SM and a GT map.

object-detection Salient Object Detection +4

Transferring Knowledge from Text to Predict Disease Onset

no code implementations6 Aug 2016 Yun Liu, Kun-Ta Chuang, Fu-Wen Liang, Huey-Jen Su, Collin M. Stultz, John V. Guttag

Specifically, we use word2vec models trained on a domain-specific corpus to estimate the relevance of each feature's text description to the prediction problem.

Sequential Optimization for Efficient High-Quality Object Proposal Generation

no code implementations14 Nov 2015 Ziming Zhang, Yun Liu, Xi Chen, Yanjun Zhu, Ming-Ming Cheng, Venkatesh Saligrama, Philip H. S. Torr

We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING but significantly improves its proposal localization quality.

object-detection Object Proposal Generation

Cannot find the paper you are looking for? You can Submit a new open access paper.