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Yunhe Wang - IEEE Xplore Author Profile

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Author details

Yunhe Wang

Publications

60

Citations

7,268

Publications by Year

20132025

Co-Authors:

Aditya AroraJinyu BaiHaoran BaiFurui BaiZhenni Bi

Show All Co-Authors (253)

Yunhe Wang

Affiliation

Noah's Ark Laboratory

Huawei Technologies Co.

Ltd, HuaWei Building, No.3 Xinxi Road, ShangDi Information Industry Base, Hai-Dian District, Beijing, P.R. China

Publication Topics

Feature Maps,
Vision Transformer,
Neural Network,
Object Detection,
Semantic Segmentation,
Convolutional Neural Network,
ImageNet,
Learning Rate,
Convolutional Layers,
Deep Neural Network,
High-resolution Images,
Large-scale Datasets

Biography

Yunhe Wang received the PhD degree from Peking University, China. He is currently a senior researcher with Huawei Noah's Ark Lab. He has authored or coauthored more than 50 papers in prestigious journals and conferences. His research interests mainly include machine learning, computer vision, and efficient deep learning. Many of them are widely applied in industrial products, and received important awards. He leads the Adder Neural Networks project, which presents a new computing paradigm for artificial intelligence with significantly lower hardware costs. He is a PC/senior PC member for top conferences, such as NeurIPS, ICML, ICLR, CVPR, ICCV, IJCAI, and AAAI.(Based on document published on 18 February 2022).

Publications

60

Citations

7,268

Publications by Year

20132025

Co-Authors:

Aditya Arora
Jinyu Bai
Haoran Bai
Furui Bai
Zhenni Bi

Show All Co-Authors (253)

Author's Published Works

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Showing 1-24 of 24 results

Conferences (23)

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Michael Maire(22)
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University of Chicago(9)
TTI Chicago(4)
UC Berkeley, ICSI(3)
TTI-Chicago(3)
TTI, Chicago(2)
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(3)
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2)
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)(1)
2008 IEEE Conference on Computer Vision and Pattern Recognition(1)
2009 IEEE Conference on Computer Vision and Pattern Recognition(1)
IEEE(24)
Nashville, TN, USA(3)
Honolulu, HI, USA(2)
Madrid, ES(2)
Seattle, WA, USA(2)
Anchorage, AK, USA(1)
Convolutional Neural Network(7)
Image Segmentation(7)
Neural Network(7)
Semantic Segmentation(6)
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Results

Deciphering ‘What’ and ‘Where’ Visual Pathways from Spectral Clustering of Layer-Distributed Neural Representations

Xiao Zhang;David Yunis;Michael Maire

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

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We present an approach for analyzing grouping information contained within a neural network's activations, per-mitting extraction of spatial layout and semantic segmentation from the behavior of large pre-trained vision models. Unlike prior work, our method conducts a who lis tic analysis of a network's activation state, leveraging features from all layers and obviating the need to guess which par...Show More

Deciphering ‘What’ and ‘Where’ Visual Pathways from Spectral Clustering of Layer-Distributed Neural Representations

Xiao Zhang;David Yunis;Michael Maire

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Drop-Connect as a Fault-Tolerance Approach for RRAM-based Deep Neural Network Accelerators

Mingyuan Xiang;Xuhan Xie;Pedro Savarese;Xin Yuan;Michael Maire;Yanjing Li

2024 IEEE 42nd VLSI Test Symposium (VTS)

Year: 2024 | Conference Paper |

Cited by: Papers (1)

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Resistive random-access memory (RRAM) is widely recognized as a promising emerging hardware platform for deep neural networks (DNNs). Yet, due to manufacturing limitations, current RRAM devices are highly susceptible to hardware defects, which poses a significant challenge to their practical applicability. In this paper, we present a machine learning technique that enables the deployment of defect...Show More

Drop-Connect as a Fault-Tolerance Approach for RRAM-based Deep Neural Network Accelerators

Mingyuan Xiang;Xuhan Xie;Pedro Savarese;Xin Yuan;Michael Maire;Yanjing Li

2024 IEEE 42nd VLSI Test Symposium (VTS)

Year: 2024 | Conference Paper |

Automated Testing of Software that Uses Machine Learning APIs

Chengcheng Wan;Shicheng Liu;Sophie Xie;Yifan Liu;Henry Hoffmann;Michael Maire;Shan Lu

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)

Year: 2022 | Conference Paper |

Cited by: Papers (6)

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An increasing number of software applications incorporate machine learning (ML) solutions for cognitive tasks that statistically mimic human behaviors. To test such software, tremendous human effort is needed to design image/text/audio inputs that are relevant to the software, and to judge whether the software is processing these inputs as most human beings do. Even when misbehavior is exposed, it...Show More

Automated Testing of Software that Uses Machine Learning APIs

Chengcheng Wan;Shicheng Liu;Sophie Xie;Yifan Liu;Henry Hoffmann;Michael Maire;Shan Lu

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)

Year: 2022 | Conference Paper |

Boosting Contrastive Self-Supervised Learning with False Negative Cancellation

Tri Huynh;Simon Kornblith;Matthew R. Walter;Michael Maire;Maryam Khademi

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2022 | Conference Paper |

Cited by: Papers (75)

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Self-supervised representation learning has made significant leaps fueled by progress in contrastive learning, which seeks to learn transformations that embed positive input pairs nearby, while pushing negative pairs far apart. While positive pairs can be generated reliably (e.g., as different views of the same image), it is difficult to accurately establish negative pairs, defined as samples from...Show More

Boosting Contrastive Self-Supervised Learning with False Negative Cancellation

Tri Huynh;Simon Kornblith;Matthew R. Walter;Michael Maire;Maryam Khademi

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2022 | Conference Paper |

Domain-Independent Dominance of Adaptive Methods

Pedro Savarese;David McAllester;Sudarshan Babu;Michael Maire

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Cited by: Papers (7)

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From a simplified analysis of adaptive methods, we derive AvaGrad, a new optimizer which outperforms SGD on vision tasks when its adaptability is properly tuned. We observe that the power of our method is partially explained by a decoupling of learning rate and adaptability, greatly simplifying hyperparameter search. In light of this observation, we demonstrate that, against conventional wisdom, A...Show More

Domain-Independent Dominance of Adaptive Methods

Pedro Savarese;David McAllester;Sudarshan Babu;Michael Maire

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Information-Theoretic Segmentation by Inpainting Error Maximization

Pedro Savarese;Sunnie S. Y. Kim;Michael Maire;Greg Shakhnarovich;David McAllester

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Cited by: Papers (12)

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We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maximally independent sets. More specifically, we group image pixels into foreground and background, with the goal of minimizing predictability of one set from the other. An easily computed loss drives a greedy search proces...Show More

Information-Theoretic Segmentation by Inpainting Error Maximization

Pedro Savarese;Sunnie S. Y. Kim;Michael Maire;Greg Shakhnarovich;David McAllester

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Multimodal Contrastive Training for Visual Representation Learning

Xin Yuan;Zhe Lin;Jason Kuen;Jianming Zhang;Yilin Wang;Michael Maire;Ajinkya Kale;Baldo Faieta

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Cited by: Papers (80)

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We develop an approach to learning visual representations that embraces multimodal data, driven by a combination of intra- and inter-modal similarity preservation objectives. Unlike existing visual pre-training methods, which solve a proxy prediction task in a single domain, our method exploits intrinsic data properties within each modality and semantic information from cross-modal correlation sim...Show More

Multimodal Contrastive Training for Visual Representation Learning

Xin Yuan;Zhe Lin;Jason Kuen;Jianming Zhang;Yilin Wang;Michael Maire;Ajinkya Kale;Baldo Faieta

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Are Machine Learning Cloud APIs Used Correctly?

Chengcheng Wan;Shicheng Liu;Henry Hoffmann;Michael Maire;Shan Lu

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

Year: 2021 | Conference Paper |

Cited by: Papers (18)

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Machine learning (ML) cloud APIs enable developers to easily incorporate learning solutions into software systems. Unfortunately, ML APIs are challenging to use correctly and efficiently, given their unique semantics, data requirements, and accuracy-performance tradeoffs. Much prior work has studied how to develop ML APIs or ML cloud services, but not how open-source applications are using ML APIs...Show More

Are Machine Learning Cloud APIs Used Correctly?

Chengcheng Wan;Shicheng Liu;Henry Hoffmann;Michael Maire;Shan Lu

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

Year: 2021 | Conference Paper |

A Replication of Are Machine Learning Cloud APIs Used Correctly

Chengcheng Wan;Shicheng Liu;Henry Hoffmann;Michael Maire;Shan Lu

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

Year: 2021 | Conference Paper |

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This artifact aims to provide benchmark suite, data, and script used in our study "Are Machine Learning Cloud APIs Used Correctly?". We collected a suite of 360 non-trivial applications that use ML cloud APIs for manual study. We also developed checkers and tool to detect and fix API mis-uses. We hope this artifact can motivate and help future research to further tackle ML API mis-uses. All relate...Show More

A Replication of Are Machine Learning Cloud APIs Used Correctly

Chengcheng Wan;Shicheng Liu;Henry Hoffmann;Michael Maire;Shan Lu

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

Year: 2021 | Conference Paper |

Pixel Consensus Voting for Panoptic Segmentation

Haochen Wang;Ruotian Luo;Michael Maire;Greg Shakhnarovich

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Cited by: Papers (22)

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The core of our approach, Pixel Consensus Voting, is a framework for instance segmentation based on the generalized Hough transform. Pixels cast discretized, probabilistic votes for the likely regions that contain instance centroids. At the detected peaks that emerge in the voting heatmap, backprojection is applied to collect pixels and produce instance masks. Unlike a sliding window detector that...Show More

Pixel Consensus Voting for Panoptic Segmentation

Haochen Wang;Ruotian Luo;Michael Maire;Greg Shakhnarovich

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Regularizing Deep Networks by Modeling and Predicting Label Structure

Mohammadreza Mostajabi;Michael Maire;Gregory Shakhnarovich

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Year: 2018 | Conference Paper |

Cited by: Papers (16)

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We construct custom regularization functions for use in supervised training of deep neural networks. Our technique is applicable when the ground-truth labels themselves exhibit internal structure; we derive a regularizer by learning an autoencoder over the set of annotations. Training thereby becomes a two-phase procedure. The first phase models labels with an autoencoder. The second phase trains ...Show More

Regularizing Deep Networks by Modeling and Predicting Label Structure

Mohammadreza Mostajabi;Michael Maire;Gregory Shakhnarovich

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Year: 2018 | Conference Paper |

Multigrid Neural Architectures

Tsung-Wei Ke;Michael Maire;Stella X. Yu

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2017 | Conference Paper |

Cited by: Papers (41)

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We propose a multigrid extension of convolutional neural networks (CNNs). Rather than manipulating representations living on a single spatial grid, our network layers operate across scale space, on a pyramid of grids. They consume multigrid inputs and produce multigrid outputs, convolutional filters themselves have both within-scale and cross-scale extent. This aspect is distinct from simple multi...Show More

Multigrid Neural Architectures

Tsung-Wei Ke;Michael Maire;Stella X. Yu

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2017 | Conference Paper |

Colorization as a Proxy Task for Visual Understanding

Gustav Larsson;Michael Maire;Gregory Shakhnarovich

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2017 | Conference Paper |

Cited by: Papers (301)

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We investigate and improve self-supervision as a drop-in replacement for ImageNet pretraining, focusing on automatic colorization as the proxy task. Self-supervised training has been shown to be more promising for utilizing unlabeled data than other, traditional unsupervised learning methods. We build on this success and evaluate the ability of our self-supervised network in several contexts. On V...Show More

Colorization as a Proxy Task for Visual Understanding

Gustav Larsson;Michael Maire;Gregory Shakhnarovich

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2017 | Conference Paper |

Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding

Michael Maire;Takuya Narihira;Stella X. Yu

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2016 | Conference Paper |

Cited by: Papers (47) | Patents (2)

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Spectral embedding provides a framework for solving perceptual organization problems, including image segmentation and figure/ground organization. From an affinity matrix describing pairwise relationships between pixels, it clusters pixels into regions, and, using a complex-valued extension, orders pixels according to layer. We train a convolutional neural network (CNN) to directly predict the pai...Show More

Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding

Michael Maire;Takuya Narihira;Stella X. Yu

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2016 | Conference Paper |

Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression

Takuya Narihira;Michael Maire;Stella X. Yu

2015 IEEE International Conference on Computer Vision (ICCV)

Year: 2015 | Conference Paper |

Cited by: Papers (115) | Patents (2)

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We introduce a new approach to intrinsic image decomposition, the task of decomposing a single image into albedo and shading components. Our strategy, which we term direct intrinsics, is to learn a convolutional neural network (CNN) that directly predicts output albedo and shading channels from an input RGB image patch. Direct intrinsics is a departure from classical techniques for intrinsic image...Show More

Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression

Takuya Narihira;Michael Maire;Stella X. Yu

2015 IEEE International Conference on Computer Vision (ICCV)

Year: 2015 | Conference Paper |

Learning lightness from human judgement on relative reflectance

Takuya Narihira;Michael Maire;Stella X. Yu

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2015 | Conference Paper |

Cited by: Papers (48) | Patents (1)

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We develop a new approach to inferring lightness, the perceived reflectance of surfaces, from a single image. Classic methods view this problem from the perspective of intrinsic image decomposition, where an image is separated into reflectance and shading components. Rather than reason about reflectance and shading together, we learn to directly predict lightness differences between pixels. Large-...Show More

Learning lightness from human judgement on relative reflectance

Takuya Narihira;Michael Maire;Stella X. Yu

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2015 | Conference Paper |

Progressive Multigrid Eigensolvers for Multiscale Spectral Segmentation

Michael Maire;Stella X. Yu

2013 IEEE International Conference on Computer Vision

Year: 2013 | Conference Paper |

Cited by: Papers (15)

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We reexamine the role of multiscale cues in image segmentation using an architecture that constructs a globally coherent scale-space output representation. This characteristic is in contrast to many existing works on bottom-up segmentation, which prematurely compress information into a single scale. The architecture is a standard extension of Normalized Cuts from an image plane to an image pyramid...Show More

Progressive Multigrid Eigensolvers for Multiscale Spectral Segmentation

Michael Maire;Stella X. Yu

2013 IEEE International Conference on Computer Vision

Year: 2013 | Conference Paper |

Object detection and segmentation from joint embedding of parts and pixels

Michael Maire;Stella X. Yu;Pietro Perona

2011 International Conference on Computer Vision

Year: 2011 | Conference Paper |

Cited by: Papers (38)

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We present a new framework in which image segmentation, figure/ground organization, and object detection all appear as the result of solving a single grouping problem. This framework serves as a perceptual organization stage that integrates information from low-level image cues with that of high-level part detectors. Pixels and parts each appear as nodes in a graph whose edges encode both affinity...Show More

Object detection and segmentation from joint embedding of parts and pixels

Michael Maire;Stella X. Yu;Pietro Perona

2011 International Conference on Computer Vision

Year: 2011 | Conference Paper |

Occlusion boundary detection and figure/ground assignment from optical flow

Patrik Sundberg;Thomas Brox;Michael Maire;Pablo Arbeláez;Jitendra Malik

Year: 2011 | Conference Paper |

Cited by: Papers (103) | Patents (3)

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In this work, we propose a contour and region detector for video data that exploits motion cues and distinguishes occlusion boundaries from internal boundaries based on optical flow. This detector outperforms the state-of-the-art on the benchmark of Stein and Hebert, improving average precision from .58 to .72. Moreover, the optical flow on and near occlusion boundaries allows us to assign a depth...Show More

Occlusion boundary detection and figure/ground assignment from optical flow

Patrik Sundberg;Thomas Brox;Michael Maire;Pablo Arbeláez;Jitendra Malik

Year: 2011 | Conference Paper |

Contour Detection and Hierarchical Image Segmentation

Pablo Arbeláez;Michael Maire;Charless Fowlkes;Jitendra Malik

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2011 | Volume: 33, Issue: 5 | Journal Article |

Cited by: Papers (3957) | Patents (31)

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This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into ...Show More

Contour Detection and Hierarchical Image Segmentation

Pablo Arbeláez;Michael Maire;Charless Fowlkes;Jitendra Malik

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2011 | Volume: 33, Issue: 5 | Journal Article |

From contours to regions: An empirical evaluation

Pablo Arbelaez;Michael Maire;Charless Fowlkes;Jitendra Malik

2009 IEEE Conference on Computer Vision and Pattern Recognition

Year: 2009 | Conference Paper |

Cited by: Papers (281) | Patents (1)

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We propose a generic grouping algorithm that constructs a hierarchy of regions from the output of any contour detector. Our method consists of two steps, an oriented watershed transform (OWT) to form initial regions from contours, followed by construction of an ultra-metric contour map (UCM) defining a hierarchical segmentation. We provide extensive experimental evaluation to demonstrate that, whe...Show More

From contours to regions: An empirical evaluation

Pablo Arbelaez;Michael Maire;Charless Fowlkes;Jitendra Malik

2009 IEEE Conference on Computer Vision and Pattern Recognition

Year: 2009 | Conference Paper |

Using contours to detect and localize junctions in natural images

Michael Maire;Pablo Arbelaez;Charless Fowlkes;Jitendra Malik

2008 IEEE Conference on Computer Vision and Pattern Recognition

Year: 2008 | Conference Paper |

Cited by: Papers (270)

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Contours and junctions are important cues for perceptual organization and shape recognition. Detecting junctions locally has proved problematic because the image intensity surface is confusing in the neighborhood of a junction. Edge detectors also do not perform well near junctions. Current leading approaches to junction detection, such as the Harris operator, are based on 2D variation in the inte...Show More

Using contours to detect and localize junctions in natural images

Michael Maire;Pablo Arbelaez;Charless Fowlkes;Jitendra Malik

2008 IEEE Conference on Computer Vision and Pattern Recognition

Year: 2008 | Conference Paper |

SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition

Hao Zhang;A.C. Berg;M. Maire;J. Malik

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)

Year: 2006 | Conference Paper |

Cited by: Papers (612) | Patents (12)

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We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While nearest neighbor classifiers are natural in this setting, they suffer from the problem of high varian...Show More

SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition

Hao Zhang;A.C. Berg;M. Maire;J. Malik

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)

Year: 2006 | Volume: 2 | Conference Paper |

Names and faces in the news

T.L. Berg;A.C. Berg;J. Edwards;M. Maire;R. White;Yee-Whye Teh;E. Learned-Miller;D.A. Forsyth

Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.

Year: 2004 | Conference Paper |

Cited by: Papers (189) | Patents (7)

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We show quite good face clustering is possible for a dataset of inaccurately and ambiguously labelled face images. Our dataset is 44,773 face images, obtained by applying a face finder to approximately half a million captioned news images. This dataset is more realistic than usual face recognition datasets, because it contains faces captured "in the wild" in a variety of configurations with respec...Show More

Names and faces in the news

T.L. Berg;A.C. Berg;J. Edwards;M. Maire;R. White;Yee-Whye Teh;E. Learned-Miller;D.A. Forsyth

Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.

Year: 2004 | Volume: 2 | Conference Paper |

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