Ionescu, B.
* 2002: Dissimilarity measures in color spaces
* 2005: Dynamic Hand Gesture Recognition Using the Skeleton of the Hand
* 2008: Fuzzy Color-Based Approach for Understanding Animated Movies Content in the Indexing Task, A
* 2011: color-action perceptual approach to the classification of animated movies, A
* 2012: Content-Based Video Description for Automatic Video Genre Categorization
* 2014: Benchmarking result diversification in social image retrieval
* 2014: Fusion in Computer Vision: Understanding Complex Visual Content
* 2014: Hybrid Machine-Crowd Approach to Photo Retrieval Result Diversification, A
* 2015: Cluster encoding for modelling temporal variation in video
* 2015: distortion evaluation framework in 3D video view synthesis, A
* 2016: 3D Human pose estimation: A review of the literature and analysis of covariates
* 2016: Boosting VLAD with double assignment using deep features for action recognition in videos
* 2016: Event-Based Media Processing and Analysis: A Survey of the Literature
* 2016: Fisher Kernel Temporal Variation-based Relevance Feedback for video retrieval
* 2016: Multiview Plus Depth Video Coding With Temporal Prediction View Synthesis
* 2016: Special Issue on Event-based Media Processing and Analysis
* 2017: Benchmarking Initiative for Multimedia Evaluation: MediaEval 2016, The
* 2017: Spatio-Temporal Vector of Locally Max Pooled Features for Action Recognition in Videos
* 2017: Spatio-Temporal VLAD Encoding for Human Action Recognition in Videos
* 2018: Overview of the Multimedia Information Processing for Personality and Social Networks Analysis Contest
* 2019: Computational Understanding of Visual Interestingness Beyond Semantics: Literature Survey and Analysis of Covariates
* 2020: Artificial Intelligence Fights Crime and Terrorism at a New Level
* 2021: Benchmarking Image Retrieval Diversification Techniques for Social Media
* 2021: Deepfusion: Deep Ensembles for Domain Independent System Fusion
* 2021: Visual Interestingness Prediction: A Benchmark Framework and Literature Review
* 2022: Affect in Multimedia: Benchmarking Violent Scenes Detection
* 2022: Face Verification with Challenging Imposters and Diversified Demographics
Includes: Ionescu, B. Ionescu, B.[Bogdan]
27 for Ionescu, B.
Ionescu, C.[Catalin]
* 2009: Structural SVM for visual localization and continuous state estimation
* 2010: Random Fourier Approximations for Skewed Multiplicative Histogram Kernels
* 2011: Latent structured models for human pose estimation
* 2014: Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
* 2014: Iterated Second-Order Label Sensitive Pooling for 3D Human Pose Estimation
* 2014: NIMRAD: novel technique for respiratory data treatment
* 2015: Matrix Backpropagation for Deep Networks with Structured Layers
Includes: Ionescu, C.[Catalin] Ionescu, C.
7 for Ionescu, C.
Ionescu, C.M.[Clara M.]
* 2012: Robustness evaluation of fractional order control for varying time delay processes
* 2012: Special issue: Fractional signals and systems
* 2013: Analysis of the Respiratory Dynamics During Normal Breathing by Means of Pseudophase Plots and Pressure-Volume Loops
Includes: Ionescu, C.M.[Clara M.] Ionescu, C.M.
Ionescu, D.
* 1996: 3D Reconstruction Using an Uncalibrated Stereo Pair of Encoded Images
* 1999: high precision 3D object reconstruction method using a color coded grid and NURBS, A
Ionescu, L.[Liviu]
* 2023: Radar Signal Processor of the First Romanian Space Surveillance Radar, The
Ionescu, R.T.[Radu Tudor]
* 2013: Kernels for Visual Words Histograms
* 2013: Speeding Up Local Patch Dissimilarity
* 2014: Objectness to improve the bag of visual words model
* 2015: Have a SNAK. Encoding Spatial Information with the Spatial Non-alignment Kernel
* 2015: PQ kernel: A rank correlation kernel for visual word histograms
* 2016: How Hard Can It Be? Estimating the Difficulty of Visual Search in an Image
* 2017: Deep Appearance Features for Abnormal Behavior Detection in Video
* 2017: Unmasking the Abnormal Events in Video
* 2018: Frustratingly Easy Trade-off Optimization Between Single-Stage and Two-Stage Deep Object Detectors
* 2019: Clustering Images by Unmasking: A New Baseline
* 2019: Detecting Abnormal Events in Video Using Narrowed Normality Clusters
* 2019: Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video
* 2020: Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)
* 2021: Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
* 2021: Automotive Radar Interference Mitigation with Unfolded Robust PCA based on Residual Overcomplete Auto-Encoder Blocks
* 2021: Contextual Convolutional Neural Networks
* 2021: Curriculum self-paced learning for cross-domain object detection
* 2021: SuPEr-SAM: Using the Supervision Signal from a Pose Estimator to Train a Spatial Attention Module for Personal Protective Equipment Recognition
* 2021: Teacher-Student Training and Triplet Loss for Facial Expression Recognition under Occlusion
* 2021: Unsupervised Medical Image Alignment With Curriculum Learning
* 2022: Background-Agnostic Framework With Adversarial Training for Abnormal Event Detection in Video, A
* 2022: Curriculum Learning: A Survey
* 2022: Discriminability-enforcing loss to improve representation learning
* 2022: Feature-level Augmentation to Improve Robustness of Deep Neural Networks to Affine Transformations
* 2022: Self-paced Learning to Improve Text Row Detection in Historical Documents with Missing Labels
* 2022: Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
* 2022: UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection
* 2023: Audiovisual Masked Autoencoders
* 2023: Diffusion Models in Vision: A Survey
* 2023: JEDI: Joint Expert Distillation in a Semi-Supervised Multi-Dataset Student-Teacher Scenario for Video Action Recognition
* 2023: Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution
* 2023: Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results
* 2023: SSMTL++: Revisiting self-supervised multi-task learning for video anomaly detection
* 2024: CL-MAE: Curriculum-Learned Masked Autoencoders
* 2024: Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection
Includes: Ionescu, R.T.[Radu Tudor] Ionescu, R.T.
35 for Ionescu, R.T.