What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Feature extraction.
His Artificial intelligence study is mostly concerned with Discriminative model, Object detection, Image segmentation, Segmentation and Feature.
His Feature study combines topics in areas such as Principal component analysis and Feature detection.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Cognitive neuroscience of visual object recognition, Data mining, Gesture recognition, Histogram and Visual Word.
The study incorporates disciplines such as Class, Field, Annotation and Latency in addition to Machine learning.
His Feature extraction research incorporates elements of Contextual image classification, Training set and Convolutional neural network.
His most cited work include:
- Large-Scale Video Classification with Convolutional Neural Networks (3601 citations)
- PCA-SIFT: a more distinctive representation for local image descriptors (2753 citations)
- Large-scale Video Classification with Convolutional Neural Networks (1230 citations)
What are the main themes of his work throughout his whole career to date?
Rahul Sukthankar mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Object.
His Artificial intelligence study focuses mostly on Discriminative model, Classifier, Segmentation, Training set and Feature extraction.
His Computer vision course of study focuses on Computer graphics and Digital camera.
The Pattern recognition study combines topics in areas such as Contextual image classification, Histogram, Cognitive neuroscience of visual object recognition and Cluster analysis.
Machine learning is frequently linked to Conditional random field in his study.
His Object research includes themes of Consistency and Representation.
He most often published in these fields:
- Artificial intelligence (78.33%)
- Computer vision (39.17%)
- Pattern recognition (22.08%)
What were the highlights of his more recent work (between 2017-2020)?
- Artificial intelligence (78.33%)
- Computer vision (39.17%)
- Artificial neural network (5.42%)
In recent papers he was focusing on the following fields of study:
Rahul Sukthankar mainly investigates Artificial intelligence, Computer vision, Artificial neural network, Convolutional neural network and Robot.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Concept learning, Motion planning and Rendering.
His Convolutional neural network research is multidisciplinary, incorporating elements of Object, Motion, RGB color model and Optical flow.
His Robot study combines topics in areas such as Natural user interface and Human–computer interaction.
His Object detection research includes elements of Variation, Feature extraction and Benchmark.
Between 2017 and 2020, his most popular works were:
- AVA: A Video Dataset of Spatio-Temporally Localized Atomic Visual Actions (322 citations)
- Rethinking the Faster R-CNN Architecture for Temporal Action Localization (272 citations)
- Actor-Centric Relation Network (110 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Rahul Sukthankar focuses on Artificial intelligence, Computer vision, Convolutional neural network, Optical flow and Action recognition.
His Artificial intelligence research incorporates themes from Key and Pattern recognition.
His research in Key intersects with topics in Variation, Image segmentation, Object detection, Benchmark and Feature extraction.
His Pattern recognition research is multidisciplinary, relying on both Consistency, Pose and Facial expression.
The various areas that he examines in his Convolutional neural network study include RGB color model, Motion and Inference.
Rahul Sukthankar combines subjects such as Artificial neural network, Frame and Relation with his study of Visualization.
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.
