D-Index & Metrics Best Publications
Computer Science
Italy
2023

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 88 Citations 27,985 576 World Ranking 403 National Ranking 3

Research.com Recognitions

Awards & Achievements

2023 - Research.com Computer Science in Italy Leader Award

2022 - Research.com Computer Science in Italy Leader Award

2012 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to human behaviour understanding and multimedia

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

Nicu Sebe spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Feature extraction. All of his Artificial intelligence and Feature, Facial expression, Support vector machine, Facial recognition system and Image retrieval investigations are sub-components of the entire Artificial intelligence study. The Classifier, Wavelet transform and Naive Bayes classifier research Nicu Sebe does as part of his general Pattern recognition study is frequently linked to other disciplines of science, such as Multi-task learning, therefore creating a link between diverse domains of science.

Nicu Sebe combines subjects such as Training set and Data mining with his study of Machine learning. His studies deal with areas such as Image processing, Discriminative model and Feature selection as well as Feature extraction. His research in Feature selection tackles topics such as Dimensionality reduction which are related to areas like Multimedia.

His most cited work include:

  • Content-based multimedia information retrieval: State of the art and challenges (1413 citations)
  • Facial expression recognition from video sequences: temporal and static modeling (761 citations)
  • Multimodal human-computer interaction: A survey (698 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Multimedia. Image, Feature extraction, Deep learning, Feature and Image retrieval are the core of his Artificial intelligence study. His Pattern recognition study incorporates themes from Object detection and Representation.

His research in Computer vision focuses on subjects like Facial expression, which are connected to Face and Facial recognition system. His research on Machine learning often connects related topics like Contextual image classification. Nicu Sebe has included themes like Field and Session, World Wide Web in his Multimedia study.

He most often published in these fields:

  • Artificial intelligence (66.77%)
  • Pattern recognition (30.54%)
  • Computer vision (22.94%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (66.77%)
  • Pattern recognition (30.54%)
  • Image (10.76%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Image, Deep learning and Machine learning. Nicu Sebe interconnects Generator and Computer vision in the investigation of issues within Artificial intelligence. His work in Pattern recognition addresses subjects such as Feature, which are connected to disciplines such as Feature learning.

His Image study also includes fields such as

  • Representation which intersects with area such as Channel,
  • Object that intertwine with fields like Motion,
  • Generalization, which have a strong connection to Set. The various areas that he examines in his Deep learning study include Monocular, Training set, Classifier, Artificial neural network and State. His Machine learning study frequently links to adjacent areas such as Inference.

Between 2018 and 2021, his most popular works were:

  • Animating Arbitrary Objects via Deep Motion Transfer (91 citations)
  • First Order Motion Model for Image Animation (77 citations)
  • Unsupervised Domain Adaptation Using Feature-Whitening and Consensus Loss (75 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

Nicu Sebe focuses on Artificial intelligence, Pattern recognition, Image, Deep learning and Source code. The concepts of his Artificial intelligence study are interwoven with issues in Generator, Machine learning and Computer vision. His study in the fields of Ground truth and Feature under the domain of Machine learning overlaps with other disciplines such as Task analysis and Meta learning.

His work carried out in the field of Pattern recognition brings together such families of science as Frame and Representation. Nicu Sebe has researched Image in several fields, including Generative grammar, Face and Task. His research integrates issues of Classifier, Object detection, Benchmark and Monocular in his study of Deep learning.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Content-based multimedia information retrieval: State of the art and challenges

Michael S. Lew;Nicu Sebe;Chabane Djeraba;Ramesh Jain.
ACM Transactions on Multimedia Computing, Communications, and Applications (2006)

2204 Citations

Multimodal human-computer interaction: A survey

Alejandro Jaimes;Nicu Sebe.
Computer Vision and Image Understanding (2007)

1358 Citations

Facial expression recognition from video sequences: temporal and static modeling

Ira Cohen;Nicu Sebe;Ashutosh Garg;Lawrence S. Chen.
Computer Vision and Image Understanding (2003)

1172 Citations

A Survey on Learning to Hash

Jingdong Wang;Ting Zhang;Jingkuan Song;Nicu Sebe.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2018)

747 Citations

Authentic facial expression analysis

N. Sebe;M. S. Lew;Y. Sun;I. Cohen.
Image and Vision Computing (2007)

474 Citations

Learning Deep Representations of Appearance and Motion for Anomalous Event Detection

Dan Xu;Elisa Ricci;Yan Yan;Jingkuan Song.
british machine vision conference (2015)

401 Citations

Combining Head Pose and Eye Location Information for Gaze Estimation

R. Valenti;N. Sebe;T. Gevers.
IEEE Transactions on Image Processing (2012)

370 Citations

Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation

Dan Xu;Elisa Ricci;Wanli Ouyang;Xiaogang Wang.
computer vision and pattern recognition (2017)

350 Citations

Deformable GANs for Pose-Based Human Image Generation

Aliaksandr Siarohin;Enver Sangineto;Stephane Lathuiliere;Nicu Sebe.
computer vision and pattern recognition (2018)

323 Citations

Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction

I. Cohen;F.G. Cozman;N. Sebe;M.C. Cirelo.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)

320 Citations

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