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Author details

Peng Di

Publications

18

Citations

67

Publications by Year

20092024

Co-Authors:

Wenting CaiLiqing CaoYang CaoDajun ChenHongwei Chen

Show All Co-Authors (84)

Peng Di

Affiliation

Ant Group, China

Publication Topics

Call Graph,
Static Analysis,
Language Model,
Benchmark,
Code Changes,
Code Snippets,
Complete Coding,
External Dependence,
Fintech,
Function Calls,
Industrial Applications,
Information Leakage

Biography

Peng Di received the Ph.D. degree in computer science and engineering from the University of New South Wales, in 2013. He is a Professor-Level Senior Engineer with Ant Group where he leads the Program Analysis Group. He is also an Adjunct Academic Staff with UNSW Sydney and Zhejiang University. His research interests include programming languages, compiler technology, and software engineering. He played a key role in co-founding several open-source projects, including SVF, MindSpore, CodeFuse, and others.(Based on document published on 12 April 2024).

Publications

18

Citations

67

Publications by Year

20092024

Co-Authors:

Wenting Cai
Liqing Cao
Yang Cao
Dajun Chen
Hongwei Chen

Show All Co-Authors (84)

Author's Published Works

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Showing 1-25 of 31 results

Conferences (27)

Journals (4)

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David McAllester(24)
Seiichi Mita(14)
Chunzhao Guo(9)
D. McAllester(6)
Hossein Tehrani Niknejad(4)
Toyota Technological Institute, Chicago, IL, USA(8)
Toyota Technological Institute, Nagoya, Aichi, Japan(8)
Toyota Technical Institute, Chicago, IL, USA(6)
Toyota Technological Institute, Nagoya, Japan(6)
University of Chicago, USA(3)
2010 IEEE Intelligent Vehicles Symposium(2)
2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)(2)
2011 IEEE Intelligent Vehicles Symposium (IV)(2)
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(2)
IEEE Transactions on Intelligent Transportation Systems(2)
IEEE(31)
San Francisco, CA, USA(3)
Baden-Baden, Germany(2)
La Jolla, CA, USA(2)
Nashville, TN, USA(2)
Washington, DC, USA(2)
Deformation Model(9)
Energy Function(7)
Histogram Of Oriented Gradients(7)
Left Image(7)
Markov Random Field(7)

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Results

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 |

Robust Monocular Epipolar Flow Estimation

Koichiro Yamaguchi;David McAllester;Raquel Urtasun

2013 IEEE Conference on Computer Vision and Pattern Recognition

Year: 2013 | Conference Paper |

Cited by: Papers (94)

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We consider the problem of computing optical flow in monocular video taken from a moving vehicle. In this setting, the vast majority of image flow is due to the vehicle's ego-motion. We propose to take advantage of this fact and estimate flow along the epipolar lines of the egomotion. Towards this goal, we derive a slanted-plane MRF model which explicitly reasons about the ordering of planes and t...Show More

Robust Monocular Epipolar Flow Estimation

Koichiro Yamaguchi;David McAllester;Raquel Urtasun

2013 IEEE Conference on Computer Vision and Pattern Recognition

Year: 2013 | Conference Paper |

Robust Road Detection and Tracking in Challenging Scenarios Based on Markov Random Fields With Unsupervised Learning

Chunzhao Guo;Seiichi Mita;David McAllester

IEEE Transactions on Intelligent Transportation Systems

Year: 2012 | Volume: 13, Issue: 3 | Journal Article |

Cited by: Papers (53) | Patents (1)

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This paper presents a robust stereo-vision-based drivable road detection and tracking system that was designed to navigate an intelligent vehicle through challenging traffic scenarios and increment road safety in such scenarios with advanced driver-assistance systems (ADAS). This system is based on a formulation of stereo with homography as a maximum a posteriori (MAP) problem in a Markov random h...Show More

Robust Road Detection and Tracking in Challenging Scenarios Based on Markov Random Fields With Unsupervised Learning

Chunzhao Guo;Seiichi Mita;David McAllester

IEEE Transactions on Intelligent Transportation Systems

Year: 2012 | Volume: 13, Issue: 3 | Journal Article |

On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation

Hossein Tehrani Niknejad;Akihiro Takeuchi;Seiichi Mita;David McAllester

IEEE Transactions on Intelligent Transportation Systems

Year: 2012 | Volume: 13, Issue: 2 | Journal Article |

Cited by: Papers (132) | Patents (1)

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This paper proposes a novel method for multivehicle detection and tracking using a vehicle-mounted monocular camera. In the proposed method, the features of vehicles are learned as a deformable object model through the combination of a latent support vector machine (LSVM) and histograms of oriented gradients (HOGs). The detection algorithm combines both global and local features of the vehicle as ...Show More

On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation

Hossein Tehrani Niknejad;Akihiro Takeuchi;Seiichi Mita;David McAllester

IEEE Transactions on Intelligent Transportation Systems

Year: 2012 | Volume: 13, Issue: 2 | Journal Article |

Vehicle detection and tracking at nighttime for urban autonomous driving

Hossein Tehrani Niknejad;Koji Takahashi;Seiichi Mita;David McAllester

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

Year: 2011 | Conference Paper |

Cited by: Papers (6) | Patents (4)

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This paper proposes a method for on road detecting and tracking of multi vehicles at nighttime in urban environment. The features of vehicles including root and part filters are learned as a weighted deformable object model through the combination of a latent support vector machine (LSVM) and histograms of oriented gradients (HOG). Detected vehicles are tracked through a particle filter which esti...Show More

Vehicle detection and tracking at nighttime for urban autonomous driving

Hossein Tehrani Niknejad;Koji Takahashi;Seiichi Mita;David McAllester

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

Year: 2011 | Conference Paper |

Hierarchical road understanding for intelligent vehicles based on sensor fusion

Chunzhao Guo;Seiichi Mita;David McAllester

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

Year: 2011 | Conference Paper |

Cited by: Papers (4) | Patents (1)

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Comprehensive situational awareness is paramount to the effectiveness of proprietary navigational and higher-level functions of the intelligent vehicles. In this paper, we address a hierarchical road understanding system for intelligent vehicles with respect to the road topography and the existence of objects based on sensor fusion. The proposed system consists of three modules that run in paralle...Show More

Hierarchical road understanding for intelligent vehicles based on sensor fusion

Chunzhao Guo;Seiichi Mita;David McAllester

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

Year: 2011 | Conference Paper |

Vision-based vehicle detection for nighttime with discriminately trained mixture of weighted deformable part models

Hossein Tehrani Niknejad;Seiichi Mita;David McAllester;Takashi Naito

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

Year: 2011 | Conference Paper |

Cited by: Papers (19)

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Vehicle detection at night time is a challenging problem due to low visibility and light distortion caused by motion and illumination in urban environments. This paper presents a method based on the deformable object model for detecting and classifying vehicles by using monocular infra-red cameras. As some features of vehicles, such as headlight and taillights are more visible at night time, we pr...Show More

Vision-based vehicle detection for nighttime with discriminately trained mixture of weighted deformable part models

Hossein Tehrani Niknejad;Seiichi Mita;David McAllester;Takashi Naito

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

Year: 2011 | Conference Paper |

Adaptive non-planar road detection and tracking in challenging environments using segmentation-based Markov Random Field

Chunzhao Guo;Seiichi Mita;David McAllester

2011 IEEE International Conference on Robotics and Automation

Year: 2011 | Conference Paper |

Cited by: Papers (7)

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Many roads made for land vehicles are not totally planar and present uphill and downhill slopes that follow the environment topography. Moreover, the road appearance is often affected by a number of factors in challenging conditions. In this paper, we present an adaptive non-planar road detection and tracking approach which overcomes these difficulties by a piecewise planar road model as well as a...Show More

Adaptive non-planar road detection and tracking in challenging environments using segmentation-based Markov Random Field

Chunzhao Guo;Seiichi Mita;David McAllester

2011 IEEE International Conference on Robotics and Automation

Year: 2011 | Conference Paper |

PAC-Bayesian approach for minimization of phoneme error rate

Joseph Keshet;David McAllester;Tamir Hazan

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2011 | Conference Paper |

Cited by: Papers (9)

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We describe a new approach for phoneme recognition which aims at minimizing the phoneme error rate. Building on structured prediction techniques, we formulate the phoneme recognizer as a linear combination of feature functions. We state a PAC-Bayesian generalization bound, which gives an upper-bound on the expected phoneme error rate in terms of the empirical phoneme error rate. Our algorithm is d...Show More

PAC-Bayesian approach for minimization of phoneme error rate

Joseph Keshet;David McAllester;Tamir Hazan

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2011 | Conference Paper |

Embedded multi-sensors objects detection and tracking for urban autonomous driving

Hossein Tehrani Niknejad;Koji Takahashi;Seiichi Mita;David McAllester

2011 IEEE Intelligent Vehicles Symposium (IV)

Year: 2011 | Conference Paper |

Cited by: Papers (5) | Patents (1)

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This paper proposes an embedded real time method for detecting and tracking of multiobjects including vehicles, pedestrians, motorbikes and bicycles in urban environment. The features of different objects are learned as a deformable object model through the combination of a latent support vector machine (LSVM) and histograms of oriented gradients (HOG). Laser depth data have been used as a priori ...Show More

Embedded multi-sensors objects detection and tracking for urban autonomous driving

Hossein Tehrani Niknejad;Koji Takahashi;Seiichi Mita;David McAllester

2011 IEEE Intelligent Vehicles Symposium (IV)

Year: 2011 | Conference Paper |

Graph-based 2D road representation of 3D point clouds for intelligent vehicles

Chunzhao Guo;Wataru Sato;Long Han;Seiichi Mita;David McAllester

2011 IEEE Intelligent Vehicles Symposium (IV)

Year: 2011 | Conference Paper |

Cited by: Papers (35) | Patents (1)

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Comprehensive situational awareness is paramount to the effectiveness of proprietary navigational and higher-level functions of intelligent vehicles. In this paper, we address a graph-based approach for 2D road representation of 3D point clouds with respect to the road topography. We employ the gradient cues of the road geometry to construct a Markov Random Filed (MRF) and implement an efficient b...Show More

Graph-based 2D road representation of 3D point clouds for intelligent vehicles

Chunzhao Guo;Wataru Sato;Long Han;Seiichi Mita;David McAllester

2011 IEEE Intelligent Vehicles Symposium (IV)

Year: 2011 | Conference Paper |

Lane detection and tracking in challenging environments based on a weighted graph and integrated cues

Chunzhao Guo;Seiichi Mita;David McAllester

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

Year: 2010 | Conference Paper |

Cited by: Papers (5) | Patents (1)

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Real-time lane detection and localization is one of the key issues for many intelligent transportation systems. In this paper, we present a lane detection and tracking approach designed to work in challenging environments where lane boundaries may be low-contrast and changeful with noise due to a number of factors such as wear, type, lighting and weather conditions, etc. In the method, a sophistic...Show More

Lane detection and tracking in challenging environments based on a weighted graph and integrated cues

Chunzhao Guo;Seiichi Mita;David McAllester

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

Year: 2010 | Conference Paper |

A vision system for autonomous vehicle navigation in challenging traffic scenes using integrated cues

Chunzhao Guo;Seiichi Mita;David McAllester

13th International IEEE Conference on Intelligent Transportation Systems

Year: 2010 | Conference Paper |

Cited by: Papers (6)

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This paper presents a system used for navigating an autonomous vehicle through challenging traffic scenes based on stereo vision. In order to safely as well as legally facilitate autonomous driving behaviors such as road following, obstacle avoidance, off-road navigation, etc., the proposed system consists of two parallel modules: drivable road region detection and tracking module and painted lane...Show More

A vision system for autonomous vehicle navigation in challenging traffic scenes using integrated cues

Chunzhao Guo;Seiichi Mita;David McAllester

13th International IEEE Conference on Intelligent Transportation Systems

Year: 2010 | Conference Paper |

MRF-based road detection with unsupervised learning for autonomous driving in changing environments

Chunzhao Guo;Seiichi Mita;David McAllester

2010 IEEE Intelligent Vehicles Symposium

Year: 2010 | Conference Paper |

Cited by: Papers (6)

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This paper presents a vision-based approach with unsupervised learning for robust, accurate and stable detection of the drivable road to deal with autonomous driving in changing environments. This approach is based on a formulation of stereo with homography as a Maximum A Posteriori (MAP) problem in a Markov Random Field (MRF). Under this formulation, we develop an alternating optimization algorit...Show More

MRF-based road detection with unsupervised learning for autonomous driving in changing environments

Chunzhao Guo;Seiichi Mita;David McAllester

2010 IEEE Intelligent Vehicles Symposium

Year: 2010 | Conference Paper |

On-road vehicle tracking using deformable object model and particle filter with integrated likelihoods

Akihiro Takeuchi;Seiichi Mita;David McAllester

2010 IEEE Intelligent Vehicles Symposium

Year: 2010 | Conference Paper |

Cited by: Papers (30) | Patents (2)

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This paper proposes a novel method for vehicle detection and tracking using a vehicle-mounted monocular camera. In this method, features of vehicles are learned as a deformable object model through the combination of a latent support vector machine (LSVM) and histograms of oriented gradients (HOG). The vehicle detector uses both global and local features as the deformable object model. Detected ve...Show More

On-road vehicle tracking using deformable object model and particle filter with integrated likelihoods

Akihiro Takeuchi;Seiichi Mita;David McAllester

2010 IEEE Intelligent Vehicles Symposium

Year: 2010 | Conference Paper |

Structure and motion from road-driving stereo sequences

Hoang Trinh;David McAllester

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

Year: 2010 | Conference Paper |

Cited by: Papers (1) | Patents (3)

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We introduce a unified framework for scene structure and motion estimation on road-driving stereo sequences. This framework is based on the slanted-plane scene model that has become widely popular in the stereo vision community. Our algorithm iteratively and alternately solves for scene structure and motion. Surface estimation is done using our own slanted-plane stereo algorithm. Motion estimation...Show More

Structure and motion from road-driving stereo sequences

Hoang Trinh;David McAllester

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

Year: 2010 | Conference Paper |

Cascade object detection with deformable part models

Pedro F. Felzenszwalb;Ross B. Girshick;David McAllester

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Year: 2010 | Conference Paper |

Cited by: Papers (631) | Patents (13)

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We describe a general method for building cascade classifiers from part-based deformable models such as pictorial structures. We focus primarily on the case of star-structured models and show how a simple algorithm based on partial hypothesis pruning can speed up object detection by more than one order of magnitude without sacrificing detection accuracy. In our algorithm, partial hypotheses are pr...Show More

Cascade object detection with deformable part models

Pedro F. Felzenszwalb;Ross B. Girshick;David McAllester

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Year: 2010 | Conference Paper |

The Communication Complexity of Correlation

Prahladh Harsha;Rahul Jain;David McAllester;Jaikumar Radhakrishnan

IEEE Transactions on Information Theory

Year: 2010 | Volume: 56, Issue: 1 | Journal Article |

Cited by: Papers (70)

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Let X and Y be finite nonempty sets and (X,Y) a pair of random variables taking values in X?Y. We consider communication protocols between two parties, Alice and Bob, for generating X and Y. Alice is provided an x ? X generated according to the distribution of X , and is required to send a message to Bob in order to enable him to generate y ? Y, whose distribution is the same as that of Y|X=x. Bot...Show More

The Communication Complexity of Correlation

Prahladh Harsha;Rahul Jain;David McAllester;Jaikumar Radhakrishnan

IEEE Transactions on Information Theory

Year: 2010 | Volume: 56, Issue: 1 | Journal Article |

Object Detection with Discriminatively Trained Part-Based Models

Pedro F. Felzenszwalb;Ross B. Girshick;David McAllester;Deva Ramanan

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2010 | Volume: 32, Issue: 9 | Journal Article |

Cited by: Papers (7516) | Patents (139)

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We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system re...Show More

Object Detection with Discriminatively Trained Part-Based Models

Pedro F. Felzenszwalb;Ross B. Girshick;David McAllester;Deva Ramanan

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2010 | Volume: 32, Issue: 9 | Journal Article |

Stereovision-based road boundary detection for intelligent vehicles in challenging scenarios

Chunzhao Guo;Seiichi Mita;David McAllester

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

Year: 2009 | Conference Paper |

Cited by: Papers (18)

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Road detection is a crucial problem for intelligent vehicles and mobile robots. Most of the methods proposed nowadays only achieve reliable results in relatively well-arranged environments. In this paper, we proposed a stereovision-based road boundary detection method by combining homography estimation and MRF-based belief propagation to cope with challenging scenarios such as unstructured roads w...Show More

Stereovision-based road boundary detection for intelligent vehicles in challenging scenarios

Chunzhao Guo;Seiichi Mita;David McAllester

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

Year: 2009 | Conference Paper |

Drivable road region detection using homography estimation and efficient belief propagation with coordinate descent optimization

Chunzhao Guo;Seiichi Mita;David McAllester

2009 IEEE Intelligent Vehicles Symposium

Year: 2009 | Conference Paper |

Cited by: Papers (9) | Patents (1)

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Road detection is one of the key issues for the implementation of intelligent vehicles. In this paper, we present a drivable road region detection method using homography estimation and efficient belief propagation. In the method, each pixel in stereo images is assigned a label by minimizing an energy function that accounts for the planar road region, which is defined by utilizing the 2D projectiv...Show More

Drivable road region detection using homography estimation and efficient belief propagation with coordinate descent optimization

Chunzhao Guo;Seiichi Mita;David McAllester

2009 IEEE Intelligent Vehicles Symposium

Year: 2009 | Conference Paper |

A discriminatively trained, multiscale, deformable part model

Pedro Felzenszwalb;David McAllester;Deva Ramanan

2008 IEEE Conference on Computer Vision and Pattern Recognition

Year: 2008 | Conference Paper |

Cited by: Papers (1880) | Patents (23)

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This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable pa...Show More

A discriminatively trained, multiscale, deformable part model

Pedro Felzenszwalb;David McAllester;Deva Ramanan

2008 IEEE Conference on Computer Vision and Pattern Recognition

Year: 2008 | Conference Paper |

The Communication Complexity of Correlation

Prahladh Harsha;Rahul Jain;David McAllester;Jaikumar Radhakrishnan

Twenty-Second Annual IEEE Conference on Computational Complexity (CCC'07)

Year: 2007 | Conference Paper |

Cited by: Papers (40)

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We examine the communication required for generating random variables remotely. One party Alice is given a distribution D, and she has to send a message to Bob, who is then required to generate a value with distribution exactly D. Aliceand Bob are allowed to share random bits generated without the knowledge of D. There are two settings based on how the distribution D provided to Alice is chosen. I...Show More

The Communication Complexity of Correlation

Prahladh Harsha;Rahul Jain;David McAllester;Jaikumar Radhakrishnan

Twenty-Second Annual IEEE Conference on Computational Complexity (CCC'07)

Year: 2007 | Conference Paper |

A Min-Cover Approach for Finding Salient Curves

P. Felzenszwalb;D. McAllester

2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06)

Year: 2006 | Conference Paper |

Cited by: Papers (53)

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We consider the problem of deriving a global interpretation of an image in terms of a small set of smooth curves. The problem is posed using a statistical model for images with multiple curves. Besides having important applications to edge detection and grouping the curve finding task is a special case of a more general problem, where we want to explain the whole image in terms of a small set of o...Show More

A Min-Cover Approach for Finding Salient Curves

P. Felzenszwalb;D. McAllester

2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06)

Year: 2006 | Conference Paper |

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