
Computer Vision - ACCV 2018
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The six volume set LNCS 11361-11366 constitutes the proceedings of the 14 th Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018. The total of 274 contributions was carefully reviewed and selected from 979 submissions during two rounds of reviewing and improvement. The papers focus on motion and tracking, segmentation and grouping, image-based modeling, dep learning, object recognition object recognition, object detection and categorization, vision and language, video analysis and event recognition, face and gesture analysis, statistical methods and learning, performance evaluation, medical image analysis, document analysis, optimization methods, RGBD and depth camera processing, robotic vision, applications of computer vision.
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Content
- Intro
- Preface
- Organization
- Contents - Part IV
- Poster Session P2
- Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation
- 1 Introduction
- 2 Related Work
- 3 Background
- 3.1 Deep Belief Networks
- 3.2 GPLVM
- 3.3 Shape Boltzmann Machine
- 4 The GPDBN Model
- 5 Experiments
- 6 Conclusion
- References
- Gated Hierarchical Attention for Image Captioning
- 1 Introduction
- 2 Related Work
- 2.1 Image Captioning with Attention Mechanisms
- 2.2 Word-CNNs for NLP
- 3 Gated Hierarchical Attention
- 4 Experiments
- 4.1 Dataset and Preprocessing
- 4.2 Implementation Details
- 4.3 Results on Karpathy Test Split
- 4.4 Results on Online Test Set
- 4.5 Ablation Study and Analysis
- 5 Conclusion and Future Work
- References
- Dealing with Ambiguity in Robotic Grasping via Multiple Predictions
- 1 Introduction
- 2 Related Work
- 3 Methods
- 3.1 Grasp Belief Maps
- 3.2 CNN Regression
- 3.3 Multiple Grasp Predictions
- 3.4 Grasp Option Ranking
- 4 Experiments and Results
- 4.1 Dataset
- 4.2 Implementation Details
- 4.3 Grasp Detection Metric
- 4.4 Evaluation and Comparisons
- 4.5 Evaluation over Multiple Grasps
- 4.6 Generalization
- 5 Conclusion
- References
- Adaptive Visual-Depth Fusion Transfer
- 1 Introduction
- 2 Related Work
- 3 Adaptive Visual-Depth Fusion Transfer
- 3.1 Notations
- 3.2 Visual-Depth Metric Fusion
- 3.3 Adaptive Transfer
- 3.4 Reparametrization and Optimization
- 4 Experiments and Results
- 4.1 Datasets
- 4.2 Benchmarks and Settings
- 5 Experimental Results
- 5.1 Parameter Sensitivity Analysis
- 5.2 Special Cases Analysis
- 5.3 Conclusion
- References
- Solving Minimum Cost Lifted Multicut Problems by Node Agglomeration
- 1 Introduction
- 2 Related Work
- 3 Optimization Problem
- 3.1 Minimum Cost Multicut Problem
- 3.2 Minimum Cost Lifted Multicut Problem
- 4 Objectives
- 5 Proposed Approach
- 5.1 Algorithms
- 6 Experiments
- 6.1 Image Decomposition
- 6.2 Mesh Segmentation
- 6.3 ISBI 2012 Challenge
- 7 Conclusion
- References
- Robust Deep Multi-modal Learning Based on Gated Information Fusion Network
- 1 Introduction
- 2 Related Works
- 2.1 Deep Multi-modal Learning
- 2.2 Object Detection Using Multi-modal Data
- 3 Robust Deep Multi-modal Learning (R-DML)
- 3.1 R-DML Architecture
- 3.2 Training
- 4 Experimental Results
- 4.1 Datasets
- 4.2 Experimental Results on KITTI Dataset
- 4.3 Experimental Results on SUN-RGBD Dataset
- 5 Conclusions
- References
- Hardware-Aware Softmax Approximation for Deep Neural Networks
- 1 Introduction
- 2 Background and Motivation
- 3 Exploring the Search Space of Operand Bit-Width
- 4 Approximating Softmax Operation
- 5 Training with Softmax Approximation
- 6 Experiments
- 6.1 Impact of Operand Bit-Width
- 6.2 Evaluations on Softmax Approximation Variants
- 6.3 Tradeoff Between Energy/Area Cost and Accuracy
- 6.4 Evaluations on Clipped Training
- 6.5 Discussion
- 7 Related Work
- 8 Conclusions and Future Work
- References
- Video Object Segmentation with Language Referring Expressions
- 1 Introduction
- 2 Related Work
- 2.1 Grounding Natural Language Expressions
- 2.2 Video Object Segmentation
- 3 Method
- 3.1 Grounding Objects in Video by Referring Expressions
- 3.2 Pixel-Level Video Object Segmentation
- 4 Collecting Referring Expressions for Video
- 5 Evaluation of Natural Language Grounding in Video
- 5.1 DAVIS16/DAVIS17 Referring Expression Grounding
- 6 Video Object Segmentation Results
- 6.1 DAVIS16 Single Object Segmentation
- 6.2 DAVIS17 Multiple Object Segmentation
- 7 Conclusion
- References
- Nonlinear Subspace Feature Enhancement for Image Set Classification
- 1 Introduction
- 2 Related Work
- 3 Nonlinear Subspace Feature Enhancement (NSFE)
- 3.1 Structured Loss Function
- 3.2 Learning Algorithm
- 3.3 Concrete Embeddings
- 3.4 Classification
- 4 Experiments
- 4.1 YouTube Celebrities (YTC)
- 4.2 YouTube Faces (YTF)
- 4.3 Mobile Faces (MobFaces)
- 4.4 Results
- 5 Conclusion
- References
- Continual Occlusion and Optical Flow Estimation
- 1 Introduction
- 2 Related Work
- 3 ContinualFlow
- 3.1 Occlusion Estimation
- 3.2 Refinement Network
- 3.3 ContinualFlow Estimation over Image Sequence
- 3.4 Training Loss
- 4 Experiments
- 4.1 Ablation Study
- 4.2 Comparison with State of the Art
- 5 Conclusion
- References
- Adversarial Learning for Visual Storytelling with Sense Group Partition
- 1 Introduction
- 2 Related Work
- 3 Model of Adversarial Storytelling
- 3.1 Sense Group
- 3.2 Generative Model
- 3.3 Reward Model
- 4 Experiments
- 4.1 Dataset
- 4.2 Evaluation
- 5 Conclusion
- References
- Laser Scar Detection in Fundus Images Using Convolutional Neural Networks
- 1 Introduction
- 2 Related Work
- 3 A Large-Scale Dataset for Laser Scar Detection
- 4 Our Approach
- 4.1 CNNs for Laser Scar Detection
- 4.2 Transfer Learning
- 5 Evaluation
- 5.1 Experimental Setup
- 5.2 Experiments
- 6 Conclusions
- References
- Gradient-Guided DCNN for Inverse Halftoning and Image Expanding
- 1 Introduction
- 2 Background and Related Work
- 2.1 Halftoning and Inverse Halftoning
- 2.2 Image Companding
- 3 Proposed Method
- 3.1 Two-Stage DCNN
- 3.2 Loss Function and Training
- 4 Experimental Results
- 4.1 Experiment Settings
- 4.2 Inverse Halftoning
- 4.3 Image Expanding
- 4.4 Model Analysis
- 5 Conclusions
- References
- Learning from PhotoShop Operation Videos: The PSOV Dataset
- 1 Introduction
- 2 Dataset Construction Procedure
- 3 Dataset Description
- 4 Tasks and Evaluation
- 5 Methodology
- 5.1 Attention-Aware Filtering
- 5.2 Video Regularization
- 5.3 3-D CNN
- 5.4 Data Augmentation
- 6 Experiments
- 6.1 Ablation Study
- 6.2 Analysis on the Command Classification Task
- 7 Conclusion
- References
- A Joint Local and Global Deep Metric Learning Method for Caricature Recognition
- 1 Introduction
- 2 Related Work
- 2.1 Caricature Recognition
- 2.2 Deep Metric Learning
- 3 Joint Local and Global Deep Metric Learning
- 3.1 Network Structure
- 3.2 Pairwise Loss Function
- 3.3 Implementation
- 4 Experiments
- 4.1 Dataset
- 4.2 Data Preprocessing
- 4.3 Results of Different Deep Network Structures
- 4.4 Local and Global Methods
- 4.5 Indirect and Direct Fine-Tuning
- 4.6 Deep and Hand-Crafted Features
- 4.7 Deep and Shallow Metric Learning
- 5 Conclusions
- References
- Fast Single Shot Instance Segmentation
- 1 Introduction
- 2 Related Work
- 3 Fast Single Shot Instance Segmentation
- 3.1 Multi-task Design for Instance Segmentation
- 3.2 Global View of the Pipeline
- 3.3 Fusion Feature
- 3.4 SSD Head for Object Detection
- 3.5 Segmentation Sub-networks
- 3.6 Direction Map of Objects
- 3.7 Post-process for Generating Instance Mask
- 3.8 Training and Loss Functions
- 4 Experiments
- 4.1 Experiments on PASCAL VOC
- 4.2 Inference Time Comparison
- 4.3 Microsoft COCO Dataset
- 5 Conclusions
- References
- A Stable Algebraic Camera Pose Estimation for Minimal Configurations of 2D/3D Point and Line Correspondences
- 1 Introduction
- 2 Related Work
- 3 Notation and Geometrical Constraints
- 3.1 2D/3D Point Correspondence
- 3.2 2D/3D Line Correspondence
- 4 Minimal Solution
- 4.1 P3P
- 4.2 P2P1L
- 4.3 P1P2L
- 4.4 P3L
- 4.5 Solve the Rotation Matrix
- 4.6 Algorithm Summary
- 5 Simulation Results
- 5.1 Results of P3P Problem
- 5.2 Results of P2P1L and P1P2L Problem
- 5.3 Results of P3L Problem
- 5.4 Computational Time
- 6 Conclusion
- References
- Symmetry-Aware Face Completion with Generative Adversarial Networks
- 1 Introduction
- 2 Related Work
- 3 Proposed Method
- 3.1 Generator
- 3.2 Discriminator
- 3.3 Symmetry Detection for Face Components
- 3.4 Loss Functions
- 3.5 Training
- 4 Experimental Results
- 4.1 Datasets
- 4.2 Qualitative Results
- 4.3 Comparison with the State of the Art
- 4.4 Quantitative Comparison
- 4.5 Limitations and Discussion
- 5 Conclusions
- References
- GrowBit: Incremental Hashing for Cross-Modal Retrieval
- 1 Introduction
- 2 Related Work
- 3 Proposed Approach
- 3.1 Stage 1: Learning the Hash Code
- 3.2 Stage 2: Learning the Hash Functions
- 4 Experiments
- 4.1 Datasets and Evaluation Protocol
- 4.2 Baseline and Implementation Details
- 4.3 Results
- 4.4 Analysis of the Proposed Approach.
- 5 Summary
- References
- Region-Semantics Preserving Image Synthesis
- 1 Introduction
- 2 Related Work
- 3 Fast RSP Image Synthesis
- 3.1 Technical Contributions
- 4 Experiments
- 4.1 Results Given Single-Object Regions
- 4.2 Results Given Complex Regions
- 4.3 Semantics Preserving Vs. Mode Collapse
- 4.4 Quantitative Comparison
- 4.5 Effect of l and Gradient Noises
- 5 Conclusion
- References
- SemiStarGAN: Semi-supervised Generative Adversarial Networks for Multi-domain Image-to-Image Translation
- 1 Introduction
- 2 Related Work
- 3 Proposed Method
- 3.1 Formulation
- 3.2 GAN Objectice
- 3.3 Domain Classification Loss and Self-Ensembling
- 3.4 Cycle Consistency and Pseudo Cycle Consistency Loss
- 3.5 Y Model: Splitting Classifier and Discriminator
- 3.6 Full Objective
- 4 Experimental Validation
- 4.1 Evaluation Metrics
- 4.2 Implementation and Training
- 4.3 Experimental Results
- 5 Conclusion
- References
- Gated Transfer Network for Transfer Learning
- 1 Introduction
- 2 Related Work
- 3 Gated Transfer Network
- 3.1 Transfer Module
- 3.2 Interpretation
- 3.3 Auxiliary Classifier
- 3.4 Network Architecture
- 4 Experiments
- 4.1 Datasets
- 4.2 Implementation Details
- 4.3 Ablation Study
- 4.4 Visualizing Transfer Module
- 4.5 Learning Without Forgetting
- 4.6 Comparison to State-of-the-art
- 5 Conclusion
- References
- Detecting Anomalous Trajectories via Recurrent Neural Networks
- 1 Introduction
- 2 Related Work
- 2.1 Trajectory Anomaly Detection
- 2.2 Trajectory Similarity Measures
- 2.3 RNN-Based Autoencoder
- 3 Proposed Method
- 3.1 RNN Autoencoder Based Trajectory Distance
- 3.2 Distance Based Anomaly Detection
- 4 Experimental Results
- 4.1 Comparison of Distances on Different Trajectory Patterns
- 4.2 Anomaly Detection Performances
- 5 Conclusion
- References
- A Binary Optimization Approach for Constrained K-Means Clustering
- 1 Introduction
- 2 Related Work
- 3 Problem Formulation
- 3.1 K-Means Clustering
- 3.2 Constrained K-Means
- 4 Constrained K-Means as Binary Optimization
- 5 Optimization Strategy
- 5.1 Updating the Centroids
- 5.2 Updating the Assignment Matrix
- 6 Main Algorithm
- 7 Experiments
- 7.1 Balanced Clustering on Synthetic Data
- 7.2 Clustering on Real Datasets
- 8 Conclusion
- References
- LS3D: Single-View Gestalt 3D Surface Reconstruction from Manhattan Line Segments
- 1 Introduction
- 2 Prior Work
- 3 The LS3D Algorithm
- 3.1 Manhattan Line Segment Detection
- 3.2 Manhattan Tree Construction
- 3.3 Lifting 2D MTs to 3D
- 3.4 From Line Segments to Surfaces
- 3.5 Constrained L1-Minimization for Manhattan Building Reconstruction
- 4 Evaluation Dataset
- 5 Evaluation
- 6 Conclusion and Future Work
- References
- Deep Supervised Hashing with Spherical Embedding
- 1 Introduction
- 2 Related Work
- 3 Problem Overview
- 4 Hash Function Learning
- 5 Spherical Embedding
- 6 Quantization
- 7 Triplet Spherical Loss
- 7.1 Margin Loss
- 7.2 Label Likelihood Loss
- 7.3 Spring Loss
- 8 Experiments
- 8.1 Experimental Setup
- 8.2 Results
- 8.3 Ablation Study
- 9 Conclusions
- References
- Semantic Aware Attention Based Deep Object Co-segmentation
- 1 Introduction
- 2 Related Work
- 3 Model
- 3.1 Channel Wise Attention (CA)
- 3.2 Fused Channel Wise Attention (FCA)
- 3.3 Channel Spatial Attention (CSA)
- 3.4 Instant Group Co-segmentation
- 3.5 Training and Implementation Details
- 4 Experiments
- 4.1 Datasets
- 4.2 Baselines
- 4.3 Results and Visualization of Co-segmentation
- 4.4 Instant Group Co-segmentation Results
- 5 Conclusion
- References
- PIRC Net: Using Proposal Indexing, Relationships and Context for Phrase Grounding
- 1 Introduction
- 2 Related Work
- 3 Our Network
- 3.1 Framework Overview
- 3.2 Proposal Indexing Network (PIN)
- 3.3 Inter-phrase Regeression Network (IRN)
- 3.4 Proposal Ranking Network (PRN)
- 3.5 Supervised Training and Inference
- 4 Weakly Supervised Training
- 4.1 Weak Proposal Indexing Network (WPIN)
- 4.2 Training and Inference
- 5 Experiments and Results
- 5.1 Datasets
- 5.2 Experimental Setup
- 5.3 Results on Flickr30k Entities
- 5.4 Results on ReferIt Game
- 5.5 Qualitative Results
- 6 Conclusions
- References
- Paired-D GAN for Semantic Image Synthesis
- 1 Introduction
- 2 Related Work
- 3 Semantic Levels of Image Features for Foreground/Background
- 4 Proposed Method
- 4.1 Network Design
- 4.2 Network Architecture
- 4.3 Adversarial Learning for Paired-GAN
- 5 Experiments
- 5.1 Dataset and Compared Methods
- 5.2 Implementation and Training Details
- 5.3 Evaluation Metrics
- 5.4 Qualitative Evaluation
- 5.5 Quantitative Evaluation
- 5.6 Detailed Analysis
- 6 Conclusion
- References
- Skeleton Transformer Networks: 3D Human Pose and Skinned Mesh from Single RGB Image
- 1 Introduction
- 2 Related Work
- 3 Skeleton Transformer Networks
- 3.1 Bone Rotation Regressor
- 3.2 Cross Heatmap Regressor
- 3.3 Loss Function
- 4 In-the-wild 3D Human Pose Dataset
- 5 Experiments
- 5.1 Dataset and Evaluation Protocols
- 5.2 Baselines
- 5.3 Implementation and Training Detail
- 5.4 Results
- 6 Conclusion
- References
- Detecting Text in the Wild with Deep Character Embedding Network
- 1 Introduction
- 2 Related Works
- 3 Method
- 3.1 Network Design
- 3.2 Training Character Detector
- 3.3 Learning Character Embedding
- 3.4 Post-processing
- 4 Experiments
- 4.1 Datasets and Evaluation
- 4.2 Implementation Details
- 4.3 Ablation Study
- 4.4 Experiments on Scene Text Benchmarks
- 4.5 Future Works
- 5 Conclusion
- References
- Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
- 1 Introduction
- 2 Related Work
- 3 Proposed Method
- 3.1 Learning to Tag the Foreground Object
- 3.2 Unsupervised Video Object Segmentation
- 4 Experiments
- 4.1 Datasets
- 4.2 Implementation Details
- 4.3 Comparison with State-of-the-art Methods
- 4.4 Ablation Studies
- 5 Conclusion and Future Work
- References
- Identity-Enhanced Network for Facial Expression Recognition
- 1 Introduction
- 2 Related Work
- 2.1 Facial Expression Recognition
- 2.2 Multi-task Learning
- 3 Identity-Enhanced Network
- 3.1 Spatial Fusion
- 3.2 Self-constrained Multi-task Learning
- 3.3 Network Architecture
- 4 Experiments
- 4.1 Dataset Description
- 4.2 Preprocessing
- 4.3 Implementation Details
- 4.4 Results
- 4.5 Ablation Study
- 5 Conclusions
- References
- A Novel Multi-scale Invariant Descriptor Based on Contour and Texture for Shape Recognition
- 1 Introduction
- 2 The Proposed Method
- 2.1 Feature Extraction
- 2.2 Invariance of the Descriptor
- 2.3 Dissimilarity Measure
- 3 Experiments
- 3.1 Performance Comparison on COIL20 Dataset
- 3.2 Performance Comparison on Flavia Dataset
- 3.3 Performance Comparison on Swedish Dataset
- 3.4 Performance Comparison on Leaf100 Dataset
- 3.5 Performance Comparison on ETH-80 Dataset
- 4 Conclusion
- References
- PReMVOS: Proposal-Generation, Refinement and Merging for Video Object Segmentation
- 1 Introduction
- 2 Related Work
- 3 Approach
- 3.1 Image Augmentation
- 3.2 Proposal Generation
- 3.3 Proposal Refinement
- 3.4 Mask Propagation Using Optical Flow
- 3.5 ReID Embedding Vectors
- 3.6 Proposal Merging
- 4 Experiments
- 4.1 Proposal Refinement
- 4.2 Proposal Merging
- 4.3 Runtime Evaluation
- 4.4 Further Large-Scale Evaluation
- 5 Conclusion
- References
- ColorNet: Investigating the Importance of Color Spaces for Image Classification
- 1 Introduction
- 2 Related Works
- 3 Proposed Approach
- 3.1 Architecture of Model Used
- 4 Experimental Analysis
- 4.1 Datasets
- 4.2 Training
- 4.3 Classification Results on CIFAR-10
- 4.4 Classification Results on CIFAR-100
- 4.5 Classification Results on Imagenet
- 4.6 Classification Results on SVHN
- 4.7 Further Analysis of Results
- 5 Conclusion
- References
- Pooling-Based Feature Extraction and Coarse-to-fine Patch Matching for Optical Flow Estimation
- 1 Introduction
- 2 Related Work
- 3 Proposed Method
- 3.1 Pooling-Based Feature Extraction
- 3.2 Coarse-to-fine Patch Matching
- 3.3 Post-processing and Optical Flow Estimation
- 4 Experiments
- 4.1 Experimental Methods
- 4.2 Algorithm Parameters
- 4.3 Ablation Study
- 4.4 Evaluation of Patch Matching
- 4.5 Evaluation of Optical Flow Estimation
- 5 Conclusion
- References
- Oral Session O4: Detection, Segmentation, and Action
- Unseen Object Segmentation in Videos via Transferable Representations
- 1 Introduction
- 2 Related Work
- 3 Algorithmic Overview
- 3.1 Overview of the Proposed Framework
- 3.2 Objective Function
- 4 Transferring Visual Information for Segmentation
- 4.1 Mining Segment Proposals
- 4.2 Learning Transferable Feature Representations
- 4.3 Joint Formulation and Model Training
- 5 Experimental Results
- 5.1 Implementation Details
- 5.2 DAVIS Dataset
- 5.3 YouTube-Objects Dataset
- 6 Concluding Remarks
- References
- Forget and Diversify: Regularized Refinement for Weakly Supervised Object Detection
- 1 Introduction
- 2 Related Work
- 2.1 Weakly Supervised Object Detection
- 2.2 Regularization of Deep Neural Networks
- 3 Preliminaries
- 4 Our Approach
- 4.1 Multi-round Regularization of Refinement
- 4.2 Graph-Based Label Generation
- 5 Experiments
- 5.1 Implementation Details
- 5.2 Datasets and Evaluation Metrics
- 5.3 Ablation Study
- 5.4 Results on PASCAL VOC Datasets
- 6 Conclusion
- References
- Task-Adaptive Feature Reweighting for Few Shot Classification
- 1 Introduction
- 2 Related Work
- 3 Method
- 3.1 Problem
- 3.2 Baseline Method: Prototypical Network Prototypical
- 3.3 Our Method: Task-Adaptive Prototypical Network
- 4 Experiments
- 4.1 Experimental Setting
- 4.2 Few-Shot Classification on miniImageNet
- 4.3 Few-Shot Classification on tieredImageNet
- 4.4 Visualization of Generated Feature Weight
- 5 Conclusions
- References
- Deep Attention-Based Classification Network for Robust Depth Prediction
- 1 Introduction
- 2 Related Work
- 3 Method
- 3.1 Network Architecture
- 3.2 Depth Discretization Strategy
- 3.3 Learning and Inference
- 4 Experiments
- 4.1 Datasets and Metrics
- 4.2 Experimental Setting
- 4.3 Experiment Results
- 5 Discussion
- 5.1 Effect of Multi-class Classification
- 5.2 Effect of attention mechanism
- 6 Conclusion
- References
- Predicting Video Frames Using Feature Based Locally Guided Objectives
- 1 Introduction
- 2 Proposed Architecture
- 2.1 Stage-1: Feature Generation
- 2.2 Stage-2: Reconstruction
- 3 Locally Guided Gram Loss (LGGL)
- 4 Multi-scale Correlational Loss (MSCL)
- 5 Overall Objective Function
- 6 Experiments
- 6.1 Results on KTH and Weizmann
- 6.2 Results on UCF-101
- 6.3 Results on KITTI
- 6.4 Cross-Dataset Evaluation
- 7 Conclusion
- References
- A New Temporal Deconvolutional Pyramid Network for Action Detection
- 1 Introduction
- 2 Related Work
- 3 Approach
- 3.1 Video Unit Feature Extraction
- 3.2 TDPN Network
- 3.3 Training
- 4 Experiments
- 4.1 Evaluation Datasets
- 4.2 Implementation Details
- 4.3 Ablation Study
- 4.4 Comparison with the State of the Art
- 5 Conclusions
- References
- Dynamic Temporal Pyramid Network: A Closer Look at Multi-scale Modeling for Activity Detection
- 1 Introduction
- 2 Related Work
- 3 Approach
- 3.1 Pyramidal Input Feature Extraction with Dynamic Sampling
- 3.2 Multi-scale Feature Hierarchy with Two-Branch Network
- 3.3 Local and Global Temporal Contexts
- 4 Experiments
- 4.1 Experimental Settings
- 4.2 Implementation Details
- 4.3 Comparison with State-of-the-Art
- 4.4 Ablation Study
- 5 Conclusions
- References
- Global Regularizer and Temporal-Aware Cross-Entropy for Skeleton-Based Early Action Recognition
- 1 Introduction
- 2 Related Works
- 3 Proposed Method
- 3.1 Global Regularization
- 3.2 Temporal-Aware Cross-Entropy
- 3.3 Network Training and Action Inference
- 4 Experiments
- 4.1 Datasets
- 4.2 Experimental Settings
- 4.3 Results on the NTU Dataset
- 4.4 Results on the CMU Dataset
- 4.5 Results on the SYSU 3DHOI Dataset
- 4.6 Comparison with Pair-Wise Distance
- 4.7 Parameter Analysis
- 5 Conclusion
- References
- Correction to: Symmetry-Aware Face Completion with Generative Adversarial Networks
- Correction to: Chapter "Symmetry-Aware Face Completion with Generative Adversarial Networks" in: C. V. Jawahar et al. (Eds.): Computer Vision - ACCV 2018, LNCS 11364, https://doi.org/10.1007/978-3-030-20870-7_18
- Author Index
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