Search Results for author: Alan L. Yuille

Found 85 papers, 19 papers with code

Data, Assemble: Leveraging Multiple Datasets with Heterogeneous and Partial Labels

1 code implementation25 Sep 2021 Mintong Kang, Yongyi Lu, Alan L. Yuille, Zongwei Zhou

The success of deep learning relies heavily on large datasets with extensive labels, but we often only have access to several small, heterogeneous datasets associated with partial labels, particularly in the field of medical imaging.

DeepLab2: A TensorFlow Library for Deep Labeling

1 code implementation17 Jun 2021 Mark Weber, Huiyu Wang, Siyuan Qiao, Jun Xie, Maxwell D. Collins, Yukun Zhu, Liangzhe Yuan, Dahun Kim, Qihang Yu, Daniel Cremers, Laura Leal-Taixe, Alan L. Yuille, Florian Schroff, Hartwig Adam, Liang-Chieh Chen

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision.

Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction

no code implementations31 May 2021 Yan Wang, Peng Tang, Yuyin Zhou, Wei Shen, Elliot K. Fishman, Alan L. Yuille

We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background; For local MIL based semi-supervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier.

Multiple Instance Learning

Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining

no code implementations9 Mar 2021 Jieneng Chen, Ke Yan, Yu-Dong Zhang, YouBao Tang, Xun Xu, Shuwen Sun, Qiuping Liu, Lingyun Huang, Jing Xiao, Alan L. Yuille, Ya zhang, Le Lu

(2) The sampled deep vertex features with positional embedding are mapped into a sequential space and decoded by a multilayer perceptron (MLP) for semantic classification.

Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation

no code implementations30 Nov 2020 Qihao Liu, Weichao Qiu, Weiyao Wang, Gregory D. Hager, Alan L. Yuille

We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori, and then adapt it to the task of category-independent articulated object pose estimation.

Optical Flow Estimation Pose Estimation

Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples

no code implementations29 Oct 2020 Yingwei Li, Zhuotun Zhu, Yuyin Zhou, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille

Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to the limited amount of annotated 3D data and limited computational resources.

Pancreas Segmentation Volumetric Medical Image Segmentation

Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors

no code implementations4 Apr 2020 Zhuotun Zhu, Yongyi Lu, Wei Shen, Elliot K. Fishman, Alan L. Yuille

This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the abdominal CT scans.

Classification General Classification

CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks

1 code implementation28 Mar 2020 Qihang Yu, Yingwei Li, Jieru Mei, Yuyin Zhou, Alan L. Yuille

3D Convolution Neural Networks (CNNs) have been widely applied to 3D scene understanding, such as video analysis and volumetric image recognition.

3D Medical Imaging Segmentation Action Recognition +2

C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation

no code implementations CVPR 2020 Qihang Yu, Dong Yang, Holger Roth, Yutong Bai, Yixiao Zhang, Alan L. Yuille, Daguang Xu

3D convolution neural networks (CNN) have been proved very successful in parsing organs or tumours in 3D medical images, but it remains sophisticated and time-consuming to choose or design proper 3D networks given different task contexts.

Medical Image Segmentation Neural Architecture Search

Car Pose in Context: Accurate Pose Estimation with Ground Plane Constraints

no code implementations9 Dec 2019 Pengfei Li, Weichao Qiu, Michael Peven, Gregory D. Hager, Alan L. Yuille

Scene context is a powerful constraint on the geometry of objects within the scene in cases, such as surveillance, where the camera geometry is unknown and image quality may be poor.

Car Pose Estimation

Deep Distance Transform for Tubular Structure Segmentation in CT Scans

no code implementations CVPR 2020 Yan Wang, Xu Wei, Fengze Liu, Jieneng Chen, Yuyin Zhou, Wei Shen, Elliot K. Fishman, Alan L. Yuille

Tubular structure segmentation in medical images, e. g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases.

Adversarial Examples for Edge Detection: They Exist, and They Transfer

no code implementations2 Jun 2019 Christian Cosgrove, Alan L. Yuille

Convolutional neural networks have recently advanced the state of the art in many tasks including edge and object boundary detection.

Boundary Detection Edge Detection +3

Patch-based 3D Human Pose Refinement

no code implementations20 May 2019 Qingfu Wan, Weichao Qiu, Alan L. Yuille

State-of-the-art 3D human pose estimation approaches typically estimate pose from the entire RGB image in a single forward run.

Pose Prediction

Thickened 2D Networks for Efficient 3D Medical Image Segmentation

no code implementations2 Apr 2019 Qihang Yu, Yingda Xia, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille

With this design, we achieve a higher performance while maintaining a lower inference latency on a few abdominal organs from CT scans, in particular when the organ has a peculiar 3D shape and thus strongly requires contextual information, demonstrating our method's effectiveness and ability in capturing 3D information.

Medical Image Segmentation

Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses

1 code implementation ECCV 2020 Yingwei Li, Song Bai, Cihang Xie, Zhenyu Liao, Xiaohui Shen, Alan L. Yuille

We observe the property of regional homogeneity in adversarial perturbations and suggest that the defenses are less robust to regionally homogeneous perturbations.

Object Detection Semantic Segmentation

CRAVES: Controlling Robotic Arm with a Vision-based Economic System

1 code implementation CVPR 2019 Yiming Zuo, Weichao Qiu, Lingxi Xie, Fangwei Zhong, Yizhou Wang, Alan L. Yuille

We also construct a vision-based control system for task accomplishment, for which we train a reinforcement learning agent in a virtual environment and apply it to the real-world.

3D Pose Estimation Domain Adaptation

Elastic Boundary Projection for 3D Medical Image Segmentation

2 code implementations CVPR 2019 Tianwei Ni, Lingxi Xie, Huangjie Zheng, Elliot K. Fishman, Alan L. Yuille

The key observation is that, although the object is a 3D volume, what we really need in segmentation is to find its boundary which is a 2D surface.

3D Medical Imaging Segmentation

Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning

1 code implementation CVPR 2019 Chen Wei, Lingxi Xie, Xutong Ren, Yingda Xia, Chi Su, Jiaying Liu, Qi Tian, Alan L. Yuille

We consider spatial contexts, for which we solve so-called jigsaw puzzles, i. e., each image is cut into grids and then disordered, and the goal is to recover the correct configuration.

General Classification Image Classification +3

Snapshot Distillation: Teacher-Student Optimization in One Generation

no code implementations CVPR 2019 Chenglin Yang, Lingxi Xie, Chi Su, Alan L. Yuille

Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting.

Image Classification Object Detection +1

Generalized Coarse-to-Fine Visual Recognition with Progressive Training

no code implementations29 Nov 2018 Xutong Ren, Lingxi Xie, Chen Wei, Siyuan Qiao, Chi Su, Jiaying Liu, Qi Tian, Elliot K. Fishman, Alan L. Yuille

Computer vision is difficult, partly because the desired mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn.

Curriculum Learning Image Classification +2

Phase Collaborative Network for Two-Phase Medical Image Segmentation

no code implementations28 Nov 2018 Huangjie Zheng, Lingxi Xie, Tianwei Ni, Ya zhang, Yan-Feng Wang, Qi Tian, Elliot K. Fishman, Alan L. Yuille

However, in medical image analysis, fusing prediction from two phases is often difficult, because (i) there is a domain gap between two phases, and (ii) the semantic labels are not pixel-wise corresponded even for images scanned from the same patient.

Medical Image Segmentation

Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma

no code implementations9 Jul 2018 Zhuotun Zhu, Yingda Xia, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille

We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans.

General Classification

Deep Nets: What have they ever done for Vision?

no code implementations10 May 2018 Alan L. Yuille, Chenxi Liu

We argue that Deep Nets in their current form are unlikely to be able to overcome the fundamental problem of computer vision, namely how to deal with the combinatorial explosion, caused by the enormous complexity of natural images, and obtain the rich understanding of visual scenes that the human visual achieves.

Joint Shape Representation and Classification for Detecting PDAC

no code implementations27 Apr 2018 Fengze Liu, Lingxi Xie, Yingda Xia, Elliot K. Fishman, Alan L. Yuille

Shape representation and classification are performed in a joint manner, both to exploit the knowledge that PDAC often changes the shape of the pancreas and to prevent over-fitting.

Classification General Classification

Abdominal multi-organ segmentation with organ-attention networks and statistical fusion

no code implementations23 Apr 2018 Yan Wang, Yuyin Zhou, Wei Shen, Seyoun Park, Elliot K. Fishman, Alan L. Yuille

To address these challenges, we introduce a novel framework for multi-organ segmentation by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity.

Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training

no code implementations7 Apr 2018 Yuyin Zhou, Yan Wang, Peng Tang, Song Bai, Wei Shen, Elliot K. Fishman, Alan L. Yuille

In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expensive, and slow to obtain.

Semantic Segmentation

Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound

no code implementations7 Apr 2018 Yan Wang, Yuyin Zhou, Peng Tang, Wei Shen, Elliot K. Fishman, Alan L. Yuille

Based on the fact that very hard samples might have annotation errors, we propose a new sample selection policy, named Relaxed Upper Confident Bound (RUCB).

Medical Image Segmentation

Multi-Scale Spatially-Asymmetric Recalibration for Image Classification

no code implementations ECCV 2018 Yan Wang, Lingxi Xie, Siyuan Qiao, Ya zhang, Wenjun Zhang, Alan L. Yuille

Convolution is spatially-symmetric, i. e., the visual features are independent of its position in the image, which limits its ability to utilize contextual cues for visual recognition.

Classification General Classification +1

Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net

no code implementations2 Apr 2018 Yingda Xia, Lingxi Xie, Fengze Liu, Zhuotun Zhu, Elliot K. Fishman, Alan L. Yuille

There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation.

NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction

1 code implementation CVPR 2019 Yuan Gao, Jiayi Ma, Mingbo Zhao, Wei Liu, Alan L. Yuille

In this paper, we propose a novel Convolutional Neural Network (CNN) structure for general-purpose multi-task learning (MTL), which enables automatic feature fusing at every layer from different tasks.

Multi-Task Learning

Single-Shot Object Detection with Enriched Semantics

no code implementations CVPR 2018 Zhishuai Zhang, Siyuan Qiao, Cihang Xie, Wei Shen, Bo wang, Alan L. Yuille

Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module.

Object Detection Semantic Segmentation

Adversarial Attacks Beyond the Image Space

no code implementations CVPR 2019 Xiaohui Zeng, Chenxi Liu, Yu-Siang Wang, Weichao Qiu, Lingxi Xie, Yu-Wing Tai, Chi Keung Tang, Alan L. Yuille

Though image-space adversaries can be interpreted as per-pixel albedo change, we verify that they cannot be well explained along these physically meaningful dimensions, which often have a non-local effect.

Object Classification Question Answering +1

DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion

no code implementations CVPR 2018 Zhishuai Zhang, Cihang Xie, Jian-Yu Wang, Lingxi Xie, Alan L. Yuille

The first layer extracts the evidence of local visual cues, and the second layer performs a voting mechanism by utilizing the spatial relationship between visual cues and semantic parts.

Semantic Part Detection

Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation

2 code implementations CVPR 2018 Qihang Yu, Lingxi Xie, Yan Wang, Yuyin Zhou, Elliot K. Fishman, Alan L. Yuille

The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration.

Pancreas Segmentation

Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans

no code implementations22 Jun 2017 Yuyin Zhou, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille

Inspired by the high relevance between the location of a pancreas and its cystic region, we introduce extra deep supervision into the segmentation network, so that cyst segmentation can be improved with the help of relatively easier pancreas segmentation.

Pancreas Segmentation

NormFace: L2 Hypersphere Embedding for Face Verification

3 code implementations21 Apr 2017 Feng Wang, Xiang Xiang, Jian Cheng, Alan L. Yuille

We show that both strategies, and small variants, consistently improve performance by between 0. 2% to 0. 4% on the LFW dataset based on two models.

Face Verification Metric Learning

Multi-Context Attention for Human Pose Estimation

2 code implementations CVPR 2017 Xiao Chu, Wei Yang, Wanli Ouyang, Cheng Ma, Alan L. Yuille, Xiaogang Wang

We further combine the holistic attention model, which focuses on the global consistency of the full human body, and the body part attention model, which focuses on the detailed description for different body parts.

Pose Estimation

Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections

no code implementations8 Feb 2017 Ehsan Jahangiri, Alan L. Yuille

We propose a method to generate multiple diverse and valid human pose hypotheses in 3D all consistent with the 2D detection of joints in a monocular RGB image.

A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans

3 code implementations25 Dec 2016 Yuyin Zhou, Lingxi Xie, Wei Shen, Yan Wang, Elliot K. Fishman, Alan L. Yuille

Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans.

Pancreas Segmentation

SURGE: Surface Regularized Geometry Estimation from a Single Image

no code implementations NeurIPS 2016 Peng Wang, Xiaohui Shen, Bryan Russell, Scott Cohen, Brian Price, Alan L. Yuille

This paper introduces an approach to regularize 2. 5D surface normal and depth predictions at each pixel given a single input image.

Object Recognition with and without Objects

1 code implementation20 Nov 2016 Zhuotun Zhu, Lingxi Xie, Alan L. Yuille

While recent deep neural networks have achieved a promising performance on object recognition, they rely implicitly on the visual contents of the whole image.

Object Recognition

Semi-Supervised Sparse Representation Based Classification for Face Recognition with Insufficient Labeled Samples

no code implementations12 Sep 2016 Yuan Gao, Jiayi Ma, Alan L. Yuille

This is based on recent work on sparsity where faces are represented in terms of two dictionaries: a gallery dictionary consisting of one or more examples of each person, and a variation dictionary representing linear nuisance variables (e. g., different lighting conditions, different glasses).

Face Recognition General Classification +1

Exploiting Symmetry and/or Manhattan Properties for 3D Object Structure Estimation from Single and Multiple Images

no code implementations CVPR 2017 Yuan Gao, Alan L. Yuille

By assuming an orthographic projection model, this paper addresses the estimation of 3D structures and camera projection using symmetry and/or Manhattan structure cues, which occur when the input is single- or multiple-image from the same category, e. g., multiple different cars.

Structure from Motion

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

44 code implementations2 Jun 2016 Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille

ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales.

Semantic Segmentation

One Shot Learning via Compositions of Meaningful Patches

no code implementations ICCV 2015 Alex Wong, Alan L. Yuille

The task of discriminating one object from another is almost trivial for a human being.

One-Shot Learning

Weakly- and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

no code implementations ICCV 2015 George Papandreou, Liang-Chieh Chen, Kevin P. Murphy, Alan L. Yuille

Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.

Semantic Segmentation

Scene-Domain Active Part Models for Object Representation

no code implementations ICCV 2015 Zhou Ren, Chaohui Wang, Alan L. Yuille

In this paper, we are interested in enhancing the expressivity and robustness of part-based models for object representation, in the common scenario where the training data are based on 2D images.

Viewpoint Estimation

PASCAL Boundaries: A Class-Agnostic Semantic Boundary Dataset

no code implementations25 Nov 2015 Vittal Premachandran, Boyan Bonev, Alan L. Yuille

In this paper, we address the boundary detection task motivated by the ambiguities in current definition of edge detection.

Boundary Detection Edge Detection

DeePM: A Deep Part-Based Model for Object Detection and Semantic Part Localization

no code implementations23 Nov 2015 Jun Zhu, Xianjie Chen, Alan L. Yuille

In this paper, we propose a deep part-based model (DeePM) for symbiotic object detection and semantic part localization.

Object Detection

Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net

no code implementations21 Nov 2015 Fangting Xia, Peng Wang, Liang-Chieh Chen, Alan L. Yuille

To tackle these difficulties, we propose a "Hierarchical Auto-Zoom Net" (HAZN) for object part parsing which adapts to the local scales of objects and parts.

Attention to Scale: Scale-aware Semantic Image Segmentation

no code implementations CVPR 2016 Liang-Chieh Chen, Yi Yang, Jiang Wang, Wei Xu, Alan L. Yuille

We adapt a state-of-the-art semantic image segmentation model, which we jointly train with multi-scale input images and the attention model.

Semantic Segmentation

Towards Unified Depth and Semantic Prediction From a Single Image

no code implementations CVPR 2015 Peng Wang, Xiaohui Shen, Zhe Lin, Scott Cohen, Brian Price, Alan L. Yuille

By allowing for interactions between the depth and semantic information, the joint network provides more accurate depth prediction than a state-of-the-art CNN trained solely for depth prediction [5].

Depth Estimation Semantic Segmentation

Modeling Deformable Gradient Compositions for Single-Image Super-Resolution

no code implementations CVPR 2015 Yu Zhu, Yanning Zhang, Boyan Bonev, Alan L. Yuille

Based on the fact that singular primitive patches are more invariant to the scale change (i. e. have less ambiguity across different scales), we represent the non-singular primitives as compositions of singular ones, each of which is allowed some deformation.

Image Super-Resolution

Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation

2 code implementations9 Feb 2015 George Papandreou, Liang-Chieh Chen, Kevin Murphy, Alan L. Yuille

Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.

Semi-Supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

Representing Data by a Mixture of Activated Simplices

no code implementations12 Dec 2014 Chunyu Wang, John Flynn, Yizhou Wang, Alan L. Yuille

We show that under this restriction, building a model with simplices amounts to constructing a convex hull inside the sphere whose boundary facets is close to the data.

Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm

no code implementations NeurIPS 2014 Jun Zhu, Junhua Mao, Alan L. Yuille

We propose a novel learning algorithm called \emph{expectation loss SVM} (e-SVM) that is devoted to the problems where only the ``positiveness" instead of a binary label of each training sample is available.

Object Detection Semantic Segmentation

Explain Images with Multimodal Recurrent Neural Networks

no code implementations4 Oct 2014 Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Alan L. Yuille

In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images.

Learning Deep Structured Models

no code implementations9 Jul 2014 Liang-Chieh Chen, Alexander G. Schwing, Alan L. Yuille, Raquel Urtasun

Towards this goal, we propose a training algorithm that is able to learn structured models jointly with deep features that form the MRF potentials.

Multi-class Classification

The Secrets of Salient Object Segmentation

no code implementations CVPR 2014 Yin Li, Xiaodi Hou, Christof Koch, James M. Rehg, Alan L. Yuille

The dataset design bias does not only create the discomforting disconnection between fixations and salient object segmentation, but also misleads the algorithm designing.

Semantic Segmentation

Robust Estimation of 3D Human Poses from a Single Image

no code implementations CVPR 2014 Chunyu Wang, Yizhou Wang, Zhouchen Lin, Alan L. Yuille, Wen Gao

We address the challenges in three ways: (i) We represent a 3D pose as a linear combination of a sparse set of bases learned from 3D human skeletons.

3D Pose Estimation Action Recognition

Modeling Image Patches with a Generic Dictionary of Mini-Epitomes

no code implementations CVPR 2014 George Papandreou, Liang-Chieh Chen, Alan L. Yuille

As an alternative, we develop a generative model for the raw intensity of image patches and show that it can support image classification performance on par with optimized SIFT-based techniques in a bag-of-visual-words setting.

Classification General Classification +1

Beat the MTurkers: Automatic Image Labeling from Weak 3D Supervision

1 code implementation CVPR 2014 Liang-Chieh Chen, Sanja Fidler, Alan L. Yuille, Raquel Urtasun

Labeling large-scale datasets with very accurate object segmentations is an elaborate task that requires a high degree of quality control and a budget of tens or hundreds of thousands of dollars.

Autonomous Driving

Robust Estimation of Nonrigid Transformation for Point Set Registration

no code implementations CVPR 2013 Jiayi Ma, Ji Zhao, Jinwen Tian, Zhuowen Tu, Alan L. Yuille

In the second step, we estimate the transformation using a robust estimator called L 2 E. This is the main novelty of our approach and it enables us to deal with the noise and outliers which arise in the correspondence step.

Robust Region Grouping via Internal Patch Statistics

no code implementations CVPR 2013 Xiaobai Liu, Liang Lin, Alan L. Yuille

In this work, we present an efficient multi-scale low-rank representation for image segmentation.

Semantic Segmentation

Complexity of Representation and Inference in Compositional Models with Part Sharing

no code implementations16 Jan 2013 Alan L. Yuille, Roozbeh Mottaghi

This paper describes serial and parallel compositional models of multiple objects with part sharing.

Functional form of motion priors in human motion perception

no code implementations NeurIPS 2010 Hongjing Lu, Tungyou Lin, Alan Lee, Luminita Vese, Alan L. Yuille

We then measured human performance for motion tasks and found that we obtained better fit for the L1-norm (Laplace) than for the L2-norm (Gaussian).

Motion Estimation

Gaussian sampling by local perturbations

no code implementations NeurIPS 2010 George Papandreou, Alan L. Yuille

We present a technique for exact simulation of Gaussian Markov random fields (GMRFs), which can be interpreted as locally injecting noise to each Gaussian factor independently, followed by computing the mean/mode of the perturbed GMRF.

A unified model of short-range and long-range motion perception

no code implementations NeurIPS 2010 Shuang Wu, Xuming He, Hongjing Lu, Alan L. Yuille

The human vision system is able to effortlessly perceive both short-range and long-range motion patterns in complex dynamic scenes.

The Noisy-Logical Distribution and its Application to Causal Inference

no code implementations NeurIPS 2007 Hongjing Lu, Alan L. Yuille

We describe a novel noisy-logical distribution for representing the distribution of a binary output variable conditioned on multiple binary input variables.

Causal Inference

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