Yuille, A.[Alan]
* 2023: 3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation
* 2023: Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape
* 2023: CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
* 2023: Diffusion Models as Masked Autoencoders
* 2023: InstMove: Instance Motion for Object-centric Video Segmentation
* 2023: Masked Autoencoders Enable Efficient Knowledge Distillers
* 2023: Multispectral Video Semantic Segmentation: A Benchmark Dataset and Baseline
* 2023: PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation
* 2023: SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training
* 2024: Learning Part Segmentation from Synthetic Animals
* 2024: Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis
* 2024: Robust Category-Level 3D Pose Estimation from Diffusion-Enhanced Synthetic Data
12 for Yuille, A.
Yuille, A.L.
* 1900: Statistical and Geometrical Approaches to Visual Motion Analysis
* 1983: Extremum Principle for Shape from Contour, An
* 1983: Extremum Principle for Shape from Contour, An
* 1983: Fingerprint Theorems for Zero-Crossings
* 1983: Scaling Theorems for Zero-Crossings
* 1983: Smoothest Velocity Field and Token Matching Schemes, The
* 1984: Extremum Principle for Shape from Contour, An
* 1984: Fingerprints Theorems
* 1984: Generalized Ordering Constraint for Stereo Correspondence, A
* 1984: Scaling Theorems for Zero-Crossings
* 1985: Describing Surfaces
* 1985: Describing Surfaces
* 1985: Regularized Solution to Edge Detection, A
* 1986: Direct Passive Navigation: Analytical Solution for Quadratic Patches
* 1986: Perspective Projection Invariants
* 1986: Scaling Theorems for Zero-Crossings
* 1987: 3D Symmetry-Curvature Duality Theorems
* 1987: Massively Parallel Implementations of Theories for Apparent Motion
* 1987: Non-Rigid Motion and Regge Calculus
* 1987: Rigidity and Smoothness of Motion
* 1987: Scaling Theorems for Zero-Crossings
* 1987: Shape from Shading, Occlusion and Texture
* 1987: Stereopsis And Eye-Movement
* 1988: Creation of Structure in Dynamic Shape, The
* 1988: Determining the Optimal Weights in Multiple Objective Function Optimization
* 1988: Motion Coherence Theory, The
* 1988: Regularized Solution to Edge Detection, A
* 1988: Scaling and Fingerprint Theorems for Zero-Crossings
* 1988: Stereo and Eye Movement
* 1989: Depth Recovery Algorithm Using Defocus Information, A
* 1989: Feature Extraction from Faces Using Deformable Templates
* 1989: Mathematical Analysis of the Motion Coherence Theory, A
* 1989: Zero Crossings on Lines of Curvature
* 1990: 3D Symmetry-Curvature Duality Theorems
* 1990: Common Framework for Image Segmentation, A
* 1990: Data Fusion for Sensory Information Processing Systems
* 1990: Deformable templates for feature extraction from medical images
* 1990: Generalized Deformable Models, Statistical Physics and Matching Problems
* 1990: model for the estimate of local velocity, A
* 1990: Shape from Shading via the Fusion of Specular and Lambertian Image Components
* 1990: Stereo and Controlled Movement
* 1990: Stereo integration, mean field theory and psychophysics
* 1991: Common Framework for Image Segmentation, A
* 1991: Deformable Templates for Face Recognition
* 1991: Deformable Templates, Robust Statistics, and Hough Transforms
* 1991: Sources from Shading
* 1992: Active Vision
* 1992: Feature Extraction from Faces Using Deformable Templates
* 1992: Grouping Iso-Velocity Points for Ego-Motion Recovery
* 1992: Occlusions and Binocular Stereo
* 1992: Statistical Morphology and Bayesian Reconstruction
* 1992: Texture Segmentation by Minimizing Vector-Valued Energy Functionals: The Coupled-Membrane Model
* 1993: Impossible Shaded Images
* 1993: Local, Global, and Multilevel Stereo Matching
* 1993: Mean-Field Phase Transistions and Correlation Functions for Gibbs Random Fields
* 1994: FORMS: A Flexible Object Recognition and Modelling System
* 1994: framework for shape representation and recognition, A
* 1994: Training a general purpose deformable template
* 1995: 5+/-2 Eigenimages Suffice: An Empirical Investigation of Low-Dimensional Lighting Models
* 1995: FORMS: A Flexible Object Recognition and Modelling System
* 1995: Multilevel Enhancement and Detection of Stereo Disparity Surfaces
* 1995: Occlusions and Binocular Stereo
* 1995: Recovering Object Surfaces from Viewed Changes in Surface Texture Patterns
* 1995: Region Competition and its Analysis: A Unified Theory for Image Segmentation
* 1995: Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
* 1996: FORMS: A Flexible Object Recognition and Modelling System
* 1996: Learning Object Representations from Lighting Variations
* 1996: Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
* 1997: Bas-Relief Ambiguity, The
* 1997: Relating image warping to 3D geometrical deformations
* 1997: Shape and Albedo from Multiple Images Using Integrability
* 1997: Surface Shape from Warping
* 1998: Efficient Optimization of a Deformable Template Using Dynamic Programming
* 1998: Image Warping for Shape Recovery and Recognition
* 1998: Segmenting by Seeking the Symmetry Axis
* 1998: Signfinder: Using Color to Detect, Localize and Identify Informational Signs
* 1998: Visual Motion Estimation and Prediction: A Probabilistic Network Model for Temporal Coherence
* 1999: Bas-Relief Ambiguity, The
* 1999: Determining Generative Models of Objects Under Varying Illumination: Shape and Albedo from Multiple Images Using SVD and Integrability
* 1999: Fundamental Bounds on Edge Detection: An Information Theoretic Evaluation of Different Edge Cues
* 1999: High-Level and Generic Models for Visual Search: When Does High Level Knowledge Help?
* 1999: Manhattan World: Compass Direction from a Single Image by Bayesian Inference
* 2000: A* perspective on deterministic optimization for deformable templates, An
* 2000: Efficient Deformable Template Detection and Localization without User Initialization
* 2000: Fundamental Limits of Bayesian Inference: Order Parameters and Phase Transitions for Road Tracking
* 2000: Guest Editorial: Statistical and Computational Theories of Vision: Modeling, Learning, Sampling and Computing, Part I
* 2000: Order Parameters for Minimax Entropy Distributions: When does High Level Knowledge Help?
* 2000: Statistical Cues for Domain Specific Image Segmentation with Performance Analysis
* 2001: Double-Loop Algorithm to Minimize the Bethe and Kikuchi Free Energies, A
* 2001: Double-Loop Algorithm to Minimize the Bethe Free Energy, A
* 2001: G-factors: Relating Distributions on Features to Distributions on Images
* 2001: KGBR Viewpoint-Lighting Ambiguity and its Resolution by Generic Constraints, The
* 2001: Order Parameters for Detecting Target Curves in Images: When Does High Level Knowledge Help?
* 2003: Algorithms from statistical physics for generative models of images
* 2003: bayesian network framework for relational shape matching, A
* 2003: Generative Model Based Approach to Motion Segmentation, A
* 2003: generic viewpoint assumption and planar bias, The
* 2003: Image Parsing: Unifying Segmentation, Detection, and Recognition
* 2003: KGBR viewpointlighting ambiguity, The
* 2003: statistical approach to multi-scale edge detection, A
* 2003: Statistical edge detection: learning and evaluating edge cues
* 2004: Detecting and reading text in natural scenes
* 2004: Motion Estimation by Swendsen-Wang Cuts
* 2004: Shape Matching and Recognition: Using Generative Models and Informative Features
* 2005: Image Parsing: Unifying Segmentation, Detection, and Recognition
* 2005: Time-Efficient Cascade for Real-Time Object Detection: With applications for the visually impaired, A
* 2006: Bottom-Up and Top-down Object Detection using Primal Sketch Features and Graphical Models
* 2006: Image Parsing: Unifying Segmentation, Detection, and Recognition
* 2007: Automated Extraction of the Cortical Sulci Based on a Supervised Learning Approach
* 2007: Detecting Object Boundaries Using Low-, Mid-, and High-level Information
* 2007: Unsupervised Learning of Object Deformation Models
* 2008: Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification
* 2008: Graph-shifts: Natural image labeling by dynamic hierarchical computing
* 2008: Max Margin AND/OR Graph learning for parsing the human body
* 2008: MRF Labeling with a Graph-Shifts Algorithm
* 2008: Scale invariance without scale selection
* 2008: Shape matching and registration by data-driven EM
* 2008: Structure-perceptron learning of a hierarchical log-linear model
* 2008: Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition
* 2008: Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion
* 2009: HOP: Hierarchical object parsing
* 2009: Inference and learning with hierarchical compositional models
* 2009: Recursive compositional models: Representation, learning, and inference
* 2009: Unsupervised Learning of Probabilistic Grammar-Markov Models for Object Categories
* 2009: Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation
* 2010: Active Mask Hierarchies for Object Detection
* 2010: Detecting Object Boundaries Using Low-, Mid-, and High-level Information
* 2010: Latent hierarchical structural learning for object detection
* 2010: Learning a Hierarchical Deformable Template for Rapid Deformable Object Parsing
* 2010: Part and appearance sharing: Recursive Compositional Models for multi-view, Multi-Object Detection
* 2011: AdaBoost for Text Detection in Natural Scene
* 2011: compositional approach to learning part-based models of objects, A
* 2011: Computing importance of 2D contour parts by reconstructability
* 2011: Efficient variational inference in large-scale Bayesian compressed sensing
* 2011: Inference and Learning with Hierarchical Shape Models
* 2011: Large sample group independent component analysis of functional magnetic resonance imaging using anatomical atlas-based reduction and bootstrapped clustering
* 2011: Learning a dictionary of deformable patches using GPUs
* 2011: Max Margin Learning of Hierarchical Configural Deformable Templates (HCDTs) for Efficient Object Parsing and Pose Estimation
* 2011: Perturb-and-MAP random fields: Using discrete optimization to learn and sample from energy models
* 2011: Recursive Compositional Models for Vision: Description and Review of Recent Work
* 2011: Towards a theory of compositional learning and encoding of objects
* 2012: Computer vision needs a core and foundations
* 2012: Recursive Segmentation and Recognition Templates for Image Parsing
* 2013: Adaptive occlusion state estimation for human pose tracking under self-occlusions
* 2013: Approach to Pose-Based Action Recognition, An
* 2013: Bottom-Up Segmentation for Top-Down Detection
* 2013: Boundary Detection Benchmarking: Beyond F-Measures
* 2013: Learning a Dictionary of Shape Epitomes with Applications to Image Labeling
* 2013: Robust Estimation of Nonrigid Transformation for Point Set Registration
* 2013: Robust Region Grouping via Internal Patch Statistics
* 2014: Active Patch Model for Real World Texture and Appearance Classification, An
* 2014: Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts
* 2014: Empirical Minimum Bayes Risk Prediction: How to Extract an Extra Few % Performance from Vision Models with Just Three More Parameters
* 2014: Fast and Simple Algorithm for Producing Candidate Regions, A
* 2014: Guest Editorial: Geometry, Lighting, Motion, and Learning
* 2014: Modeling Image Patches with a Generic Dictionary of Mini-epitomes
* 2014: Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency
* 2014: Robust Estimation of 3D Human Poses from a Single Image
* 2014: Robust Point Matching via Vector Field Consensus
* 2014: Role of Context for Object Detection and Semantic Segmentation in the Wild, The
* 2014: Scale-Space SIFT flow
* 2014: Secrets of Salient Object Segmentation, The
* 2014: Shape Reconstructability Measure of Object Part Importance with Applications to Object Detection and Localization, A
* 2014: Single Image Super-resolution Using Deformable Patches
* 2014: Towards Unified Object Detection and Semantic Segmentation
* 2015: Error Factor Analysis for Wild Scene Image-Labelling
* 2015: Joint Object and Part Segmentation Using Deep Learned Potentials
* 2015: Learning Like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images
* 2015: Modeling deformable gradient compositions for single-image super-resolution
* 2015: One Shot Learning via Compositions of Meaningful Patches
* 2015: Parsing occluded people by flexible compositions
* 2015: Region-based temporally consistent video post-processing
* 2015: Scene-Domain Active Part Models for Object Representation
* 2015: Semantic part segmentation using compositional model combining shape and appearance
* 2015: Towards unified depth and semantic prediction from a single image
* 2015: Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
* 2016: Attention to Scale: Scale-Aware Semantic Image Segmentation
* 2016: DOC: Deep OCclusion Estimation from a Single Image
* 2016: Generation and Comprehension of Unambiguous Object Descriptions
* 2016: Geometric Neural Phrase Pooling: Modeling the Spatial Co-Occurrence of Neurons
* 2016: Human-Machine CRFs for Identifying Bottlenecks in Scene Understanding
* 2016: InterActive: Inter-Layer Activeness Propagation
* 2016: Mining 3D Key-Pose-Motifs for Action Recognition
* 2016: Non-Rigid Point Set Registration by Preserving Global and Local Structures
* 2016: Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform
* 2016: Symmetric Non-rigid Structure from Motion for Category-Specific Object Structure Estimation
* 2016: UnrealCV: Connecting Computer Vision to Unreal Engine
* 2016: Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net
* 2017: Adversarial Examples for Semantic Segmentation and Object Detection
* 2017: DeepSkeleton: Learning Multi-Task Scale-Associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
* 2017: Editorial: Deep Learning for Computer Vision
* 2017: Empirical Minimum Bayes Risk Prediction
* 2017: Exploiting Symmetry and/or Manhattan Properties for 3D Object Structure Estimation from Single and Multiple Images
* 2017: Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections
* 2017: Genetic CNN
* 2017: Joint Multi-person Pose Estimation and Semantic Part Segmentation
* 2017: Multi-context Attention for Human Pose Estimation
* 2017: Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection
* 2017: PASCAL Boundaries: A Semantic Boundary Dataset with a Deep Semantic Boundary Detector
* 2017: Recurrent Multimodal Interaction for Referring Image Segmentation
* 2017: Regularizing face verification nets for pain intensity regression
* 2017: ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
* 2017: Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples
* 2017: SORT: Second-Order Response Transform for Visual Recognition
* 2018: 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation, A
* 2018: Deep Co-Training for Semi-Supervised Image Recognition
* 2018: Deep Networks Under Scene-Level Supervision for Multi-Class Geospatial Object Detection from Remote Sensing Images
* 2018: Deep Regression Forests for Age Estimation
* 2018: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
* 2018: DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection Under Partial Occlusion
* 2018: Few-Shot Image Recognition by Predicting Parameters from Activations
* 2018: Ground-Truth Data Set and Baseline Evaluations for Base-Detail Separation Algorithms at the Part Level
* 2018: Guest Editorial Introduction to the Special Issue on Large Scale and Nonlinear Similarity Learning for Intelligent Video Analysis
* 2018: Multi-scale Spatially-Asymmetric Recalibration for Image Classification
* 2018: Novel Linelet-Based Representation for Line Segment Detection, A
* 2018: Progressive Neural Architecture Search
* 2018: Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation
* 2018: Single-Shot Object Detection with Enriched Semantics
* 2018: UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision
* 2018: Weakly Supervised Region Proposal Network and Object Detection
* 2019: Adversarial Attacks Beyond the Image Space
* 2019: Alarm System for Segmentation Algorithm Based on Shape Model, An
* 2019: Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
* 2019: CLEVR-Ref+: Diagnosing Visual Reasoning With Referring Expressions
* 2019: CRAVES: Controlling Robotic Arm With a Vision-Based Economic System
* 2019: Elastic Boundary Projection for 3D Medical Image Segmentation
* 2019: ELASTIC: Improving CNNs With Dynamic Scaling Policies
* 2019: Estimation of 3D Category-Specific Object Structure: Symmetry, Manhattan and/or Multiple Images
* 2019: Feature Denoising for Improving Adversarial Robustness
* 2019: Grouped Spatial-Temporal Aggregation for Efficient Action Recognition
* 2019: Improving Transferability of Adversarial Examples With Input Diversity
* 2019: Iterative Reorganization With Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning
* 2019: Learning to Refine 3D Human Pose Sequences
* 2019: Localizing Occluders with Compositional Convolutional Networks
* 2019: NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction
* 2019: Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization
* 2019: Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation
* 2019: Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints From Limited Training Data
* 2019: Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval
* 2019: Semi-Supervised 3D Abdominal Multi-Organ Segmentation Via Deep Multi-Planar Co-Training
* 2019: Snapshot Distillation: Teacher-Student Optimization in One Generation
* 2019: Synthesizing Attributes with Unreal Engine for Fine-grained Activity Analysis
* 2019: V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation
* 2020: 3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
* 2020: Adversarial Examples for Edge Detection: They Exist, and They Transfer
* 2020: Adversarial Examples Improve Image Recognition
* 2020: Are Labels Necessary for Neural Architecture Search?
* 2020: Axial-Deeplab: Stand-alone Axial-Attention for Panoptic Segmentation
* 2020: C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
* 2020: Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion
* 2020: Compositional Convolutional Neural Networks: A Deep Architecture With Innate Robustness to Partial Occlusion
* 2020: Context-Aware Group Captioning via Self-Attention and Contrastive Features
* 2020: Deep Distance Transform for Tubular Structure Segmentation in CT Scans
* 2020: Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding
* 2020: Identity Preserve Transform: Understand What Activity Classification Models Have Learnt
* 2020: JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-modal Image Alignment of Large-scale Pathological CT Scans
* 2020: Learning From Synthetic Animals
* 2020: Neural Architecture Search for Lightweight Non-Local Networks
* 2020: Object as Hotspots: An Anchor-free 3d Object Detection Approach via Firing of Hotspots
* 2020: Patchattack: A Black-box Texture-based Attack with Reinforcement Learning
* 2020: PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
* 2020: Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans
* 2020: Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses
* 2020: Resisting Large Data Variations via Introspective Transformation Network
* 2020: Robust Face Detection via Learning Small Faces on Hard Images
* 2020: Robust Object Detection Under Occlusion With Context-Aware CompositionalNets
* 2020: STFlow: Self-Taught Optical Flow Estimation Using Pseudo Labels
* 2020: Synthesize Then Compare: Detecting Failures and Anomalies for Semantic Segmentation
* 2020: Universal Physical Camouflage Attacks on Object Detectors
* 2020: Unsupervised learning of optical flow with patch consistency and occlusion estimation
* 2021: A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation
* 2021: Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images
* 2021: Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition Under Occlusion
* 2021: CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning
* 2021: Deep Differentiable Random Forests for Age Estimation
* 2021: Deep Nets: What have They Ever Done for Vision?
* 2021: Deeply Shape-guided Cascade for Instance Segmentation
* 2021: DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
* 2021: Exploring Simple 3D Multi-Object Tracking for Autonomous Driving
* 2021: Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction
* 2021: Mask Guided Matting via Progressive Refinement Network
* 2021: MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers
* 2021: Nuisance-Label Supervision: Robustness Improvement by Free Labels
* 2021: OVIS: Occluded Video Instance Segmentation
* 2021: Progressive Stage-wise Learning for Unsupervised Feature Representation Enhancement
* 2021: Robust Instance Segmentation through Reasoning about Multi-Object Occlusion
* 2021: Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors
* 2021: Self-Supervised Pillar Motion Learning for Autonomous Driving
* 2021: ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
* 2021: Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency
* 2022: Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model
* 2022: Coarse-To-Fine Incremental Few-Shot Learning
* 2022: Context-Enhanced Stereo Transformer
* 2022: CP 2: Copy-Paste Contrastive Pretraining for Semantic Segmentation
* 2022: DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection
* 2022: Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation
* 2022: External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation With Limited Data
* 2022: Imbalanced regression for intensity series of pain expression from videos by regularizing spatio-temporal face nets
* 2022: In Defense of Image Pre-Training for Spatiotemporal Recognition
* 2022: In Defense of Online Models for Video Instance Segmentation
* 2022: k-means Mask Transformer
* 2022: Learning from Synthetic Vehicles
* 2022: Learning from Temporal Gradient for Semi-supervised Action Recognition
* 2022: Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles
* 2022: Lite Vision Transformer with Enhanced Self-Attention
* 2022: Masked Feature Prediction for Self-Supervised Visual Pre-Training
* 2022: Occluded Video Instance Segmentation: A Benchmark
* 2022: OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
* 2022: PartImageNet: A Large, High-Quality Dataset of Parts
* 2022: Point-Level Region Contrast for Object Detection Pre-Training
* 2022: Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features
* 2022: Simple Data Mixing Prior for Improving Self-Supervised Learning, A
* 2022: Simulated Adversarial Testing of Face Recognition Models
* 2022: SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering
* 2022: TransMix: Attend to Mix for Vision Transformers
* 2023: BNET: Batch Normalization With Enhanced Linear Transformation
* 2023: CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
* 2023: CoKe: Contrastive Learning for Robust Keypoint Detection
* 2023: Compositor: Bottom-Up Clustering and Compositing for Robust Part and Object Segmentation
* 2023: CORL: Compositional Representation Learning for Few-Shot Classification
* 2023: Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification
* 2023: Label-Free Liver Tumor Segmentation
* 2023: SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection
* 2023: Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
Includes: Yuille, A.L. Yuille, A.L.[Alan L.]
324 for Yuille, A.L.
Yuille, A.Y.[Alan Y.]
* 2010: Occlusion Boundary Detection Using Pseudo-depth
* 2022: CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation