Valsala, V.[Vinu]
* 2020: Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations
Valsamis, I.
* 2009: Measurement System for a Magnetostrictive Torque Sensor
Valsangkar, A.A.[Akash Anil]
* 2023: Prioritised Moderation for Online Advertising
Valsasina, P.[Paula]
* 2006: Integrated Segmentation and Classification Approach Applied to Multiple Sclerosis Analysis, An
* 2007: Incorporating Domain Knowledge Into the Fuzzy Connectedness Framework: Application to Brain Lesion Volume Estimation in Multiple Sclerosis
* 2021: Encoding Brain Networks Through Geodesic Clustering of Functional Connectivity for Multiple Sclerosis Classification
Includes: Valsasina, P.[Paula] Valsasina, P. Valsasina, P.[Paola]
Valsasna, A.
* 2002: hierarchical classification strategy for digital documents, A
Valsecchi, A.[Andrea]
* 2018: 3D-2D silhouette-based image registration for comparative radiography-based forensic identification
* 2021: Stochastic 3D rock reconstruction using GANs
Valsesia, D.[Diego]
* 2014: hardware-friendly architecture for onboard rate-controlled predictive coding of hyperspectral and multispectral images, A
* 2014: Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images, A
* 2015: Large-Scale Image Retrieval Based on Compressed Camera Identification
* 2017: Binary Adaptive Embeddings From Order Statistics of Random Projections
* 2019: Analysis of SparseHash: An efficient embedding of set-similarity via sparse projections
* 2019: High-Throughput Onboard Hyperspectral Image Compression With Ground-Based CNN Reconstruction
* 2019: Image Denoising with Graph-Convolutional Neural Networks
* 2019: ToothPic: Camera-Based Image Retrieval on Large Scales
* 2020: Deep Graph-Convolutional Image Denoising
* 2020: DeepSUM: Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
* 2020: Learning Graph-convolutional Representations for Point Cloud Denoising
* 2020: NIR Image Colorization with Graph-Convolutional Neural Networks
* 2021: Denoise and Contrast for Category Agnostic Shape Completion
* 2021: Learning Localized Representations of Point Clouds With Graph-Convolutional Generative Adversarial Networks
* 2022: Exploring the Solution Space of Linear Inverse Problems with GAN Latent Geometry
Includes: Valsesia, D.[Diego] Valsesia, D.
15 for Valsesia, D.
Valskys, V.[Vaidotas]
* 2022: Influence of Landscape Structure on Wildlife-Vehicle Collisions: Geostatistical Analysis on Hot Spot and Habitat Proximity Relations, The
Valstar, E.R.[Edward R.]
* 2002: Towards computer-assisted surgery in shoulder joint replacement
* 2002: use of Roentgen stereophotogrammetry to study micromotion of orthopaedic implants, The
Valstar, M.
* 2016: Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset, The
* 2016: ChaLearn Looking at People and Faces of the World: Face Analysis Workshop and Challenge 2016
* 2016: CNN Cascade for Landmark Guided Semantic Part Segmentation, A
* 2016: Visual Object Tracking VOT2016 Challenge Results, The
* 2017: Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation
* 2018: Deep Learned Cumulative Attribute Regression
* 2018: Guest Editorial: The Computational Face
* 2018: Human Behaviour-Based Automatic Depression Analysis Using Hand-Crafted Statistics and Deep Learned Spectral Features
* 2018: Predicting Folds in Poker Using Action Unit Detectors and Decision Trees
* 2019: Clinical Scene Segmentation with Tiny Datasets
* 2019: Dynamic Facial Models for Video-Based Dimensional Affect Estimation
* 2020: EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions
* 2020: recurrent cycle consistency loss for progressive face-to-face synthesis, A
* 2021: Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition
* 2021: Apparent Personality Recognition from Uncertainty-Aware Facial Emotion Predictions using Conditional Latent Variable Models
* 2021: Audio-Visual Predictive Coding for Self-Supervised Visual Representation Learning
* 2021: Self-supervised learning of Dynamic Representations for Static Images
* 2022: Dimensional Affect Uncertainty Modelling for Apparent Personality Recognition
* 2022: Spectral Representation of Behaviour Primitives for Depression Analysis
* 2022: Time-Continuous Audiovisual Fusion with Recurrence vs Attention for In-The-Wild Affect Recognition
* 2023: Learning Person-Specific Cognition From Facial Reactions for Automatic Personality Recognition
* 2023: Modelling Stochastic Context of Audio-Visual Expressive Behaviour With Affective Processes
* 2023: Self-Supervised Learning of Person-Specific Facial Dynamics for Automatic Personality Recognition
* 2023: Transfer Learning Approach to Heatmap Regression for Action Unit Intensity Estimation, A
* 2024: COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition
* 2024: Guest Editorial: Ethics in Affective Computing
Includes: Valstar, M. Valstar, M.[Michel]
26 for Valstar, M.
Valstar, M.F.
* 2005: Facial Action Unit Detection using Probabilistic Actively Learned Support Vector Machines on Tracked Facial Point Data
* 2006: Fully Automatic Facial Action Unit Detection and Temporal Analysis
* 2007: Combined Support Vector Machines and Hidden Markov Models for Modeling Facial Action Temporal Dynamics
* 2008: Emotionally aware automated portrait painting demonstration
* 2009: Cost-Effective Solution to Synchronized Audio-Visual Capture Using Multiple Sensors
* 2010: Detection of Concept Frames Using Clustering Multi-instance Learning, The
* 2010: Facial point detection using boosted regression and graph models
* 2011: Action unit detection using sparse appearance descriptors in space-time video volumes
* 2011: Come and have an emotional workout with sensitive artificial listeners!
* 2011: Cost-effective solution to synchronised audio-visual data capture using multiple sensors
* 2011: first facial expression recognition and analysis challenge, The
* 2011: String-based audiovisual fusion of behavioural events for the assessment of dimensional affect
* 2012: Building Autonomous Sensitive Artificial Listeners
* 2012: Fully Automatic Recognition of the Temporal Phases of Facial Actions
* 2012: Meta-Analysis of the First Facial Expression Recognition Challenge
* 2012: SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent, The
* 2013: Guided Unsupervised Learning of Mode Specific Models for Facial Point Detection in the Wild
* 2013: Local Evidence Aggregation for Regression-Based Facial Point Detection
* 2014: Decision Level Fusion of Domain Specific Regions for Facial Action Recognition
* 2014: Dynamic Appearance Descriptor Approach to Facial Actions Temporal Modeling, A
* 2014: Generalized Search Method for Multiple Competing Hypotheses in Visual Tracking, A
* 2014: MTS: A Multiple Temporal Scale Tracker Handling Occlusion and Abrupt Motion Variation
* 2015: Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection
* 2015: TRIC-track: Tracking by Regression with Incrementally Learned Cascades
* 2016: Cascaded Continuous Regression for Real-Time Incremental Face Tracking
* 2016: Cascaded regression with sparsified feature covariance matrix for facial landmark detection
* 2016: Deep learning the dynamic appearance and shape of facial action units
* 2016: L2,1-based regression and prediction accumulation across views for robust facial landmark detection
* 2017: Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data
* 2017: FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge
* 2017: Small Sample Deep Learning for Newborn Gestational Age Estimation
* 2018: Functional Regression Approach to Facial Landmark Tracking, A
* 2019: Automatic Analysis of Facial Actions: A Survey
* 2019: Postnatal gestational age estimation of newborns using Small Sample Deep Learning
Includes: Valstar, M.F. Valstar, M.F.[Michel F.]
34 for Valstar, M.F.