USA-stanford

下面是USA-stanford的视觉人物信息

Hao Su
http://ai.stanford.edu/~haosu/
http://cseweb.ucsd.edu/~haosu/
【Bio】Assistant Professor at UCSD
【Focus on】Scene understanding,3D object recognition
【Code】

  • 2017 CVPR A Point Set Generation Network for 3D Object Reconstruction from a Single Image
  • 2017 CVPR PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
  • 2017 CVPR SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation
  • 2017 CVPR Learning Shape Abstractions by Assembling Volumetric Primitives
  • 2017 CVPR Learning Non-Lambertian Object Intrinsics across ShapeNet Categories
  • 2016 NIPS FPNN: Field Probing Neural Networks for 3D Data
  • 2016 CVPR Volumetric and Multi-View CNNs for Object Classification on 3D Data
  • 2016 3DV Synthesizing Training Images for Boosting Human 3D Pose Estimation
  • 2016 ECCV ObjectNet3D: A Large Scale Database for 3D Object Recognition
  • 2016 SIGGRAPH 3D Attention-Driven Depth Acquisition for Object Identification
  • 2016 SIGGRAPH A Scalable Active Framework for Region Annotation in 3D Shape Collections
  • 2015 ICCV Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
  • 2015 SIGGRAPH Joint Embeddings of Shapes and Images via CNN Image Purification

Silvio Savarese Silvio Savarese
http://cvgl.stanford.edu/research.html
【Bio】Assistant Professor,leader of a group
【Focus on】Scene understanding,object recognition,tracking,activity recognition
【Code】

  • 2016 ECCV 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
  • 2016 CVPR Deep Metric Learning via Lifted Structured Feature Embedding
  • 2015 CVPR Enriching Object Detection with 2D-3D Registration and Continuous Viewpoint Estimation
  • 2015 IJRR Learning to Track: Online Multi-Object Tracking by Decision Making
  • 2012 CVPR Estimating the Aspect Layout of Object Categories
  • 2012 CVPR Semantic Structure from Motion with Points, Regions, and Objects
  • 2012 CVPR An Efficient Branch-and-Bound Algorithm for Optimal Human Pose EStimation
  • 2011 ICCV Detecting and Tracking People using an RGB-D Camera via Multiple Detector Fusion

Feifei Li
http://vision.stanford.edu/index.html
【Bio】Associate Professor
【Focus on】Deep learning, Scene understanding, Object detection, Tracking, Activity recognition, Scene reconstruction
【Code】

  • 2016 ECCV Visual Relationship Detection with Language Priors
  • 2016 ECCV Towards Viewpoint Invariant 3D Human Pose Estimation
  • 2016 CVPR Recurrent Attention Models for Depth-Based Person Identification
  • 2016 CVPR DenseCap: Fully Convolutional Localization Networks for Dense Captioning
  • 2015 CVPR Deep Visual-Semantic Alignments for Generating Image Descriptions
  • ImageNet Large Scale Visual Recognition Challenge
  • 2015 CVPR Deep Visual-Semantic Alignments for Generating Image Descriptions
  • 2015 CVPR Best of both worlds: human-machine collaboration for dense object annotation

Vladlen Koltun Vladlen Koltun
http://vladlen.info/
【Bio】Researcher of Intel
【Focus on】Scene modeling, scene parsing
【Code】

  • 2017 Arxiv MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
  • 2017 CoRL CARLA: An Open Urban Driving Simulator
  • 2017 PNAS Robust Continuous Clustering
  • 2017 ICCV Photographic Image Synthesis with Cascaded Refinement Networks
  • 2017 ICCV Playing for Benchmarks
  • 2017 ICCV Fast Image Processing with Fully-Convolutional Networks
  • 2017 ICCV Learning Compact Geometric Features
  • 2017 CVPR Accurate Optical Flow via Direct Cost Volume Processing
  • 2017 ICLR Learning to Act by Predicting the Future
  • 2017 PAMI Direct Sparse Odometry
  • 2016 ECCV Fast Global Registration
  • 2016 ECCV Playing for Data: Ground Truth from Computer Games
  • 2016 CVPR Feature Space Optimization for Semantic Video Segmentation
  • 2016 CVPR Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids
  • 2016 ICLR Multi-Scale Context Aggregation by Dilated Convolutions2015 CVPR Learning to Propose Objects
  • 2015 CVPR Robust Reconstruction of Indoor Scenes
  • 2014 ECCV Geodesic Object Proposals
  • 2014 CVPR Fast MRF Optimization with Application to Depth Reconstruction
  • 2014 CVPR Simultaneous Localization and Calibration: Self-Calibration of Consumer Depth Cameras
  • 2013 ICCV Elastic Fragments for Dense Scene Reconstruction
  • 2013 ICCV A Simple Model for Intrinsic Image Decomposition with Depth Cues
  • 2013 ICML Parameter Learning and Convergent Inference for Dense Random Fields
  • 2011 NIPS Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
  • 2011 TOG Metropolis Procedural Modeling

Stephen Gould Stephen Gould
http://users.cecs.anu.edu.au/~sgould/index.html
【Bio】Associate Professor, PhD from Stanford
【Focus on】Scene Understanding: Object Detection, Segmentation, and Contextual Reasoning
【Code】

  • 2017 WACV Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition
  • 2016 ECCV SPICE: Semantic Propositional Image Caption Evaluation
  • 2016 TR On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization
  • 2016 CVPR Discriminative Hierarchical Rank Pooling for Activity Recognition
  • 2016 CVPR Dynamic Image Networks for Action Recognition
  • 2015 PAMI Learning Weighted Lower Linear Envelope Potentials in Binary Markov Random Fields
  • 2014 ECCV Superpixel Graph Label Transfer with Learned Distance Metric
  • 2014 PAMI Learning Weighted Lower Linear Envelope Potentials in Binary Markov Random Fields
  • 2014 ECCV perpixel Graph Label Transfer with Learned Distance Metric
  • 2013 ISBI A Framework for Generating Realistic Synthetic Sequences of Total Internal Reflection Flourescence Microscopy Images
  • 2012 JMLR DARWIN: A Framework for Machine Learning and Computer Vision Research and Development
  • 2012 ECCV PatchMatchGraph: Building a Graph of Dense Patch Correspondences for Label Transfer
  • 2012 ACCV A Noise Tolerant Watershed Transformation with Viscous Force for Seeded Image Segmentation
  • 2011 ICML Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields
  • 2011 ICML Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields
  • Darwin
  • STAIR

Roozbeh Mottaghi Roozbeh Mottaghi
http://www.cs.stanford.edu/~roozbeh/
【Bio】Researcher at AI2, postdoc from Stanford, PhD from UC,LA
【Focus on】Scene Understanding: Object Detection, Segmentation, and Contextual Reasoning
【Code】

  • 2016 ECCV “What happens if…” Learning to Predict the Effect of Forces in Images
  • 2016 CVPR Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images
  • 2011 ICCV A Compositional Approach to Learning Part-based Models of Objects
  • 2010 TR Online Selection of Discriminative Tracking Features

Marc Levoy Marc Levoy
http://graphics.stanford.edu/projects/
【Bio】Professor, leader of a group
【Focus on】computational photography, digital camera
【Code】


 

Andrew Ng Andrew Ng
http://cs.stanford.edu/people/ang/
【Bio】Associate Professor, Chief Scientist of Baidu
【Focus on】deep learning, action recognition, machine learning, Robotics
【Code】


Daphne Koller Daphne Koller
http://robotics.stanford.edu/~koller/
【Bio】Professor
【Focus on】machine learning, probabilistic models, bayesian networks
【Code】


Youzhi Zou Youzhi Zou
http://ai.stanford.edu/~wzou/
【Bio】PhD
【Focus on】deep learning, action recognition, machine translation
【Code】

  • 2012 NIPS Deep Learning of Invariant Features via Simulated Fixations in Video
  • 2011 CVPR Learning Hierarchical Spatio-temporal Features for Action Recognition with Independent Subspace Analysis

Qianyi Zhou
http://web.stanford.edu/~qianyizh/research.html
【Bio】PhD candidate, Page not found
【Focus on】3d reconstruction
【Code】

  • Elastic Fragments for DenseScene Reconstruction
  • Simultaneous Localization andCalibration: Self-Calibration of Consumer Depth Cameras

Kilian Pohl Kilian Pohl
http://www.stanford.edu/~kpohl/
【Bio】Associate Professor
【Focus on】computational medical image analysis