H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 77 Citations 24,165 337 World Ranking 524 National Ranking 317

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Structure from motion. His Artificial intelligence study frequently draws connections between adjacent fields such as Machine learning. His work on Image restoration, Image texture and 3D reconstruction as part of his general Computer vision study is frequently connected to Radiance, thereby bridging the divide between different branches of science.

His Pattern recognition study incorporates themes from Histogram and Pose. Stefano Soatto interconnects Overfitting and Cluster analysis in the investigation of issues within Algorithm. The various areas that Stefano Soatto examines in his Structure from motion study include Bounded function, Mathematical optimization, Scale factor and Frame grabber.

His most cited work include:

  • An Invitation to 3-D Vision: From Images to Geometric Models (866 citations)
  • Dynamic Textures (820 citations)
  • An Invitation to 3-D Vision (690 citations)

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

Stefano Soatto spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Segmentation. His work focuses on many connections between Artificial intelligence and other disciplines, such as Machine learning, that overlap with his field of interest in Benchmark. Stefano Soatto has researched Computer vision in several fields, including Invariant and Robustness.

Stefano Soatto has included themes like Histogram and Image in his Pattern recognition study. The study incorporates disciplines such as Mathematical optimization and Outlier in addition to Algorithm. His Segmentation research incorporates themes from Object and Piecewise.

He most often published in these fields:

  • Artificial intelligence (71.31%)
  • Computer vision (43.85%)
  • Pattern recognition (19.88%)

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

  • Artificial intelligence (71.31%)
  • Machine learning (9.43%)
  • Pattern recognition (19.88%)

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

His main research concerns Artificial intelligence, Machine learning, Pattern recognition, Artificial neural network and Algorithm. His Artificial intelligence study combines topics from a wide range of disciplines, such as Forgetting and Computer vision. His biological study spans a wide range of topics, including Inertial frame of reference and Robustness.

His Pattern recognition research is multidisciplinary, incorporating elements of Pixel and Image. The concepts of his Artificial neural network study are interwoven with issues in Segmentation, Leverage, Regularization, Invariant and Function. His Algorithm study combines topics in areas such as Point, Stochastic gradient descent and Outlier.

Between 2018 and 2021, his most popular works were:

  • Meta-Learning With Differentiable Convex Optimization (246 citations)
  • A Baseline for Few-Shot Image Classification (80 citations)
  • Entropy-SGD: biasing gradient descent into wide valleys* (57 citations)

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

  • Artificial intelligence
  • Computer vision
  • Statistics

Stefano Soatto focuses on Artificial intelligence, Machine learning, Artificial neural network, Pattern recognition and Embedding. His Artificial intelligence research includes themes of Structure, Fisher information and Computer vision. His research in Computer vision intersects with topics in Inertial frame of reference and Robustness.

His Machine learning research integrates issues from Contextual image classification, Shot and Benchmark. The study incorporates disciplines such as Langevin dynamics, Entropy, Segmentation and Training set in addition to Artificial neural network. Stefano Soatto combines subjects such as Regularization and Image with his study of Pattern recognition.

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.

Top Publications

An Invitation to 3-D Vision: From Images to Geometric Models

Yi Ma;Stefano Soatto;Jana Koseck;S. Shankar Sastry.
(2003)

2388 Citations

Dynamic Textures

Gianfranco Doretto;Alessandro Chiuso;Ying Nian Wu;Stefano Soatto.
International Journal of Computer Vision (2003)

1152 Citations

An Invitation to 3-D Vision

Yi Ma;Stefano Soatto;Jana Košecká;S. Shankar Sastry.
(2004)

1034 Citations

Quick Shift and Kernel Methods for Mode Seeking

Andrea Vedaldi;Stefano Soatto.
european conference on computer vision (2008)

878 Citations

Class segmentation and object localization with superpixel neighborhoods

Brian Fulkerson;Andrea Vedaldi;Stefano Soatto.
international conference on computer vision (2009)

843 Citations

Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

Eagle S. Jones;Stefano Soatto.
The International Journal of Robotics Research (2011)

457 Citations

Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation

Daniel Cremers;Stanley J. Osher;Stefano Soatto.
International Journal of Computer Vision (2006)

446 Citations

Structure from motion causally integrated over time

A. Chiuso;P. Favaro;Hailin Jin;S. Soatto.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)

425 Citations

Dynamic texture recognition

P. Saisan;G. Doretto;Ying Nian Wu;S. Soatto.
computer vision and pattern recognition (2001)

409 Citations

Motion estimation via dynamic vision

S. Soatto;R. Frezza;P. Perona.
IEEE Transactions on Automatic Control (1996)

381 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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