Zitnick, C.L.[C. Lawrence]
* 1994: Active Multibaseline Stereo System with Real-time Image Acquisition, An
* 1994: Active Multibaseline Stereo Vision System with Real-Time Image Capture, An
* 1995: Multibaseline Stereo System with Active Illumination and Real-Time Image Acquisition, A
* 1996: Multi-baseline Stereo Using Surface Extraction
* 1998: Method of producing a three-dimensional image from two-dimensional images
* 1998: Volumetric Iterative Approach to Stereo Matching and Occlusion Detection, A
* 1999: Cooperative Algorithm for Stereo Matching and Occlusion Detection, A
* 2000: Cooperative Algorithm for Stereo Matching and Occlusion Detection, A
* 2005: Consistent Segmentation for Optical Flow Estimation
* 2006: Finding people in repeated shots of the same scene
* 2006: Reconstructing Occluded Surfaces Using Synthetic Apertures: Stereo, Focus and Robust Measures
* 2006: Video and Image Bayesian Demosaicing with a Two Color Image Prior
* 2007: Stereo for Image-Based Rendering using Image Over-Segmentation
* 2008: Automatic Estimation and Removal of Noise from a Single Image
* 2008: Determining Patch Saliency Using Low-Level Context
* 2008: Fourier Analysis of the 2D Screened Poisson Equation for Gradient Domain Problems
* 2008: From appearance to context-based recognition: Dense labeling in small images
* 2008: Stereo reconstruction with mixed pixels using adaptive over-segmentation
* 2009: Image deblurring and denoising using color priors
* 2009: Unsupervised learning of hierarchical spatial structures in images
* 2010: Binary Coherent Edge Descriptors
* 2010: content-aware image prior, A
* 2010: role of features, algorithms and data in visual recognition, The
* 2010: Single Image Deblurring Using Motion Density Functions
* 2011: Edge foci interest points
* 2011: Finding the weakest link in person detectors
* 2012: Exploring the Spatial Hierarchy of Mixture Models for Human Pose Estimation
* 2012: Exploring Tiny Images: The Roles of Appearance and Contextual Information for Machine and Human Object Recognition
* 2012: Image Restoration by Matching Gradient Distributions
* 2012: Memory Efficient Discriminative Approach for Location Aided Recognition, A
* 2012: role of image understanding in contour detection, The
* 2012: Seeing through the blur
* 2013: Bringing Semantics into Focus Using Visual Abstraction
* 2013: Exploring Weak Stabilization for Motion Feature Extraction
* 2013: Learning the Visual Interpretation of Sentences
* 2013: Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection
* 2013: Structured Forests for Fast Edge Detection
* 2014: Detecting Objects Using Deformation Dictionaries
* 2014: Edge Boxes: Locating Object Proposals from Edges
* 2014: Microsoft COCO: Common Objects in Context
* 2014: Predicting Object Dynamics in Scenes
* 2014: Zero-Shot Learning via Visual Abstraction
* 2015: CIDEr: Consensus-based image description evaluation
* 2015: Fast Edge Detection Using Structured Forests
* 2015: From captions to visual concepts and back
* 2015: Learning Common Sense through Visual Abstraction
* 2015: Mind's eye: A recurrent visual representation for image caption generation
* 2015: VQA: Visual Question Answering
* 2016: Adopting Abstract Images for Semantic Scene Understanding
* 2016: Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
* 2016: Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels
* 2016: Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification
* 2016: We are Humor Beings: Understanding and Predicting Visual Humor
* 2017: CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
* 2017: Inferring and Executing Programs for Visual Reasoning
* 2017: VQA: Visual Question Answering
* 2020: GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction
Includes: Zitnick, C.L.[C. Lawrence] Zitnick, C.L. Zitnick, C.L.[Charles Lawrence]
57 for Zitnick, C.L.
Zitnick, L.[Larry]
* 2006: Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video
* 2008: Internet video category recognition
* 2009: Clustering Videos by Location
* 2013: Which Edges Matter?
* 2014: Car make and model recognition using 3D curve alignment
* 2014: Car make and model recognition using 3D curve alignment
* 2017: Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?
* 2019: Order-Aware Generative Modeling Using the 3D-Craft Dataset
Includes: Zitnick, L.[Larry] Zitnick, L.
8 for Zitnick, L.
Zitnik, M.
* 2015: Data Fusion by Matrix Factorization