Index for tu_z

Tu, Z. Co Author Listing * Aggregated Residual Transformations for Deep Neural Networks
* Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration
* Cluster-Based Co-Saliency Detection
* Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer's Disease Through Automated Hippocampal Segmentation
* Deeply Supervised Salient Object Detection with Short Connections
* Integrating Bottom-Up/Top-Down for Object Recognition by Data Driven Markov Chain Monte Carlo
* Introspective Neural Networks for Generative Modeling
* Joint Sulcal Detection on Cortical Surfaces With Graphical Models and Boosted Priors
* MSR-CNN: Applying motion salient region based descriptors for action recognition
* Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods
* Salience Guided Depth Calibration for Perceptually Optimized Compressive Light Field 3D Display
* Semantic Cues Enhanced Multimodality Multistream CNN for Action Recognition
* Wasserstein Introspective Neural Networks
Includes: Tu, Z. Tu, Z.[Zhuown]
13 for Tu, Z.

Tu, Z.G.[Zhi Gang] Co Author Listing * Action-Stage Emphasized Spatiotemporal VLAD for Video Action Recognition
* combined post-filtering method to improve accuracy of variational optical flow estimation, A
* Fusing disparate object signatures for salient object detection in video
* Guided Image Filtering-Based Pan-Sharpening Method: A Case Study of GaoFen-2 Imagery
* Improved Color Patch Similarity Measure Based Weighted Median Filter
* Multi-stream CNN: Learning representations based on human-related regions for action recognition
* survey of variational and CNN-based optical flow techniques, A
* Variational method for joint optical flow estimation and edge-aware image restoration
* Weighted local intensity fusion method for variational optical flow estimation
Includes: Tu, Z.G.[Zhi Gang] Tu, Z.G.[Zhi-Gang]
9 for Tu, Z.G.

Tu, Z.J.[Zong Jie] Co Author Listing * game-theoretic design for collaborative tracking in a video camera network, A
* Game-theoretic surveillance over arbitrary floor plan using a video camera network
Includes: Tu, Z.J.[Zong Jie] Tu, Z.J.[Zong-Jie]

Tu, Z.M.[Zi Mei] Co Author Listing * Background subtraction based on circulant matrix
Includes: Tu, Z.M.[Zi Mei] Tu, Z.M.[Zi-Mei]

Tu, Z.W.[Zhuo Wen] Co Author Listing * Action Recognition with Actons
* Action-Gons: Action Recognition with a Discriminative Dictionary of Structured Elements with Varying Granularity
* Active skeleton for non-rigid object detection
* Affinity learning via self-diffusion for image segmentation and clustering
* Attentional ShapeContextNet for Point Cloud Recognition
* Auto-context and its application to high-level vision tasks
* Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
* Automated Extraction of the Cortical Sulci Based on a Supervised Learning Approach
* Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models
* Co-transduction for Shape Retrieval
* Co-Transduction for Shape Retrieval
* Detecting Object Boundaries Using Low-, Mid-, and High-level Information
* Detecting texts of arbitrary orientations in natural images
* Disagreement-Based Multi-system Tracking
* Efficient scale space auto-context for image segmentation and labeling
* Exemplar-Based Human Action Pose Correction
* Exemplar-based human action pose correction and tagging
* Feature Mining for Image Classification
* Framework for Automatic Recognition of Spatial Features from Mobile Mapping Imagery, A
* Generalizing Pooling Functions in CNNs: Mixed, Gated, and Tree
* Graph-shifts: Natural image labeling by dynamic hierarchical computing
* Harvesting Mid-level Visual Concepts from Large-Scale Internet Images
* HFS: Hierarchical Feature Selection for Efficient Image Segmentation
* Holistically-Nested Edge Detection
* Image Parsing: Unifying Segmentation, Detection, and Recognition
* Image Segmentation by Data-Driven Markov Chain Monte Carlo
* Improving Shape Retrieval by Learning Graph Transduction
* Integral Channel Features
* Integrated Framework for Image Segmentation and Perceptual Grouping, An
* Integrating contour and skeleton for shape classification
* Learning a mixture of sparse distance metrics for classification and dimensionality reduction
* Learning Based Approach for 3D Segmentation and Colon Detagging, A
* Learning based coarse-to-fine image registration
* Learning Context-Sensitive Shape Similarity by Graph Transduction
* Learning Generative Models via Discriminative Approaches
* MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation
* MRF Labeling with a Graph-Shifts Algorithm
* Multiple Clustered Instance Learning for Histopathology Cancer Image Classification, Segmentation and Clustering
* Multiple Component Learning for Object Detection
* Object Recognition Using Junctions
* One-Class Multiple Instance Learning via Robust PCA for Common Object Discovery
* Overview of the 2013 Workshop on Medical Computer Vision (MCV 2013)
* Parsing Images into Region and Curve Processes
* Parsing Images into Regions, Curves, and Curve Groups
* Pictorial multi-atlas segmentation of brain MRI
* Probabilistic 3D Polyp Detection in CT Images: The Role of Sample Alignment
* Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
* Randomness and Sparsity Induced Codebook Learning with Application to Cancer Image Classification
* Range Image Segmentation by an Effective Jump-Diffusion Method
* Regularized vector field learning with sparse approximation for mismatch removal
* Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
* Reverse Image Segmentation: A High-Level Solution to a Low-Level Task
* Robust Estimation of Nonrigid Transformation for Point Set Registration
* Robust Point Matching via Vector Field Consensus
* Scalable k-NN graph construction for visual descriptors
* Scale-Space SIFT flow
* Shape band: A deformable object detection approach
* Shape Matching and Recognition: Using Generative Models and Informative Features
* Shape matching and registration by data-driven EM
* Simultaneous Learning and Alignmennt: Multi-Instance and Multi-Pose Learning
* Sparse semi-supervised learning for perceptual grouping
* Sparse Subspace Denoising for Image Manifolds
* Stochastic Algorithm for 3D Scene Segmentation and Reconstruction, A
* Supervised Learning of Edges and Object Boundaries
* Symmetry Constraint for Foreground Extraction
* Top-Down Learning for Structured Labeling with Convolutional Pseudoprior
* Unsupervised metric fusion by cross diffusion
* Unsupervised metric learning by Self-Smoothing Operator
* Unsupervised Object Class Discovery via Saliency-Guided Multiple Class Learning
* Wavelet-Based Representation of Biological Shapes
Includes: Tu, Z.W.[Zhuo Wen] Tu, Z.W.[Zhuo-Wen]
70 for Tu, Z.W.

Tu, Z.Z.[Zheng Zheng] Co Author Listing * global and local consistent ranking model for image saliency computation, A
Includes: Tu, Z.Z.[Zheng Zheng] Tu, Z.Z.[Zheng-Zheng]

Index for "t"


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