Yuille, Alan

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

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Neural Network ApplicationsMedical Image Segmentation Techniques

Cited by 22,392

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

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv

Advanced Neural Network ApplicationsMedical Image Segmentation Techniques

Cited by 22,392

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv

Advanced Neural Network ApplicationsMedical Image Segmentation Techniques

Cited by 4,213

TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., & Zhou, Y. (2021) · arXiv

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 4,206

The Role of Context for Object Detection and Semantic Segmentation in the Wild

Mottaghi, R., Chen, X., Liu, X., Cho, N.-G., Lee, S.-W., Fidler, S., Urtasun, R., & Yuille, A. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 1,438

Object perception as Bayesian inference

Kersten, D., Mamassian, P., & Yuille, A. (2004) · Annual Review of Psychology

Visual perception and processing mechanismsBayesian Modeling and Causal Inference

Cited by 1,269

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

Qiao, S., Chen, L.-C., & Yuille, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 1,245

The Concave-Convex Procedure

Yuille, A. L., & Rangarajan, A. (2003) · Neural Computation

Stochastic Gradient Optimization TechniquesOptimization and Variational Analysis

Cited by 1,151

Feature denoising for improving adversarial robustness

Xie, C., Wu, Y., Maaten, L. V. D., Yuille, A. L., & He, K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 940

Vision as Bayesian inference: analysis by synthesis?

Yuille, A., & Kersten, D. (2006) · Trends in Cognitive Sciences

Visual perception and processing mechanismsBayesian Modeling and Causal Inference

Cited by 849

Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation

Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., & Chen, L.-C. (2020) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 809

Feature extraction from faces using deformable templates

Yuille, A. L., Cohen, D. S., & Hallinan, P. W. (2003) · Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Face recognition and analysisMedical Image Segmentation Techniques

Cited by 708

Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts

Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., & Yuille, A. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 605

Masked Feature Prediction for Self-Supervised Visual Pre-Training

Wei, C., Fan, H., Xie, S., Wu, C.-Y., Yuille, A., & Feichtenhofer, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Human Pose and Action RecognitionDomain Adaptation and Few-Shot Learning

Cited by 587

DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection

Li, Y., Yu, A. W., Meng, T., Caine, B., Ngiam, J., Peng, D., Shen, J., Lu, Y., Zhou, D., Le, Q. V., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Autonomous Vehicle Technology and SafetyAdvanced Neural Network Applications

Cited by 563

PCL: Proposal Cluster Learning for Weakly Supervised Object Detection

Tang, P., Wang, X., Bai, S., Shen, W., Bai, X., Liu, W., & Yuille, A. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 406

Large-scale pancreatic cancer detection via non-contrast CT and deep learning

Cao, K., Xia, Y., Yao, J., Han, X., Lambert, L., Zhang, T., Tang, W., Jin, G., Jiang, H., Fang, X., et al. (2023) · Nature Medicine

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 358

Bayesian decision theory and psychophysics

Yuille, A. L., & Bülthoff, H. H. (1996) · Perception as Bayesian Inference

Visual perception and processing mechanismsBayesian Modeling and Causal Inference

Cited by 250

Attention Correctness in Neural Image Captioning

Liu, C., Mao, J., Sha, F., & Yuille, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence

Multimodal Machine Learning ApplicationsVisual Attention and Saliency Detection

Cited by 210

Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue.

Chu, L. C., Park, S., Kawamoto, S., Fouladi, D. F., Shayesteh, S., Zinreich, E. S., Graves, J. S., Horton, K. M., Hruban, R. H., Yuille, A. L., et al. (2019) · American Journal of Roentgenology

Radiomics and Machine Learning in Medical ImagingPancreatic and Hepatic Oncology Research

Cited by 193

Progressive Neural Architecture Search

Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L.-J., Fei-Fei, L., Yuille, A., Huang, J., & Murphy, K. (2017) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 171

3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training

Xia, Y., Liu, F., Yang, D., Cai, J., Yu, L., Zhu, Z., Xu, D., Yuille, A., & Roth, H. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)

Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot Learning

Cited by 170

Lite Vision Transformer with Enhanced Self-Attention

Yang, C., Wang, Y., Zhang, J., Zhang, H., Wei, Z., Lin, Z., & Yuille, A. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 164

Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD

Anderson, A., Douglas, P. K., Kerr, W. T., Haynes, V. S., Yuille, A. L., Xie, J., Wu, Y. N., Brown, J. A., & Cohen, M. S. (2014) · NeuroImage

Attention Deficit Hyperactivity DisorderFunctional Brain Connectivity Studies

Cited by 136