What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Kris M. Kitani mainly focuses on Artificial intelligence, Computer vision, Machine learning, Feature extraction and Trajectory.
The study incorporates disciplines such as Human–computer interaction and Pattern recognition in addition to Artificial intelligence.
His study focuses on the intersection of Human–computer interaction and fields such as Deep learning with connections in the field of Image quality and Visualization.
His work on Object detection and Video tracking as part of general Computer vision research is frequently linked to Set and Detector, bridging the gap between disciplines.
His studies examine the connections between Machine learning and genetics, as well as such issues in Optimal control, with regards to Smoothing, Kernel and Bellman equation.
The various areas that Kris M. Kitani examines in his Feature extraction study include Contextual image classification, Histogram and Feature.
His most cited work include:
- Activity forecasting (567 citations)
- The Visual Object Tracking VOT2017 Challenge Results (285 citations)
- Fast unsupervised ego-action learning for first-person sports videos (186 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Human–computer interaction, Machine learning and Pattern recognition.
His research on Artificial intelligence frequently links to adjacent areas such as Context.
His studies deal with areas such as Overfitting and Benchmark as well as Context.
Much of his study explores Computer vision relationship to Wearable computer.
His Human–computer interaction research incorporates themes from Transfer of learning and Personalization.
His work on Leverage as part of general Machine learning research is often related to Trajectory, thus linking different fields of science.
He most often published in these fields:
- Artificial intelligence (68.22%)
- Computer vision (30.93%)
- Human–computer interaction (15.68%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (68.22%)
- Computer vision (30.93%)
- Feature (8.47%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Computer vision, Feature, Machine learning and Pattern recognition.
His research in Artificial intelligence tackles topics such as Context which are related to areas like Benchmark.
His work in Computer vision addresses subjects such as Robot, which are connected to disciplines such as Robotic arm and Eye tracking.
His research integrates issues of Artificial neural network, Feature extraction and Image description in his study of Feature.
His study looks at the intersection of Pattern recognition and topics like Generative model with Regret.
His Video tracking study incorporates themes from Kalman filter and Pipeline.
Between 2019 and 2021, his most popular works were:
- Human motion trajectory prediction: a survey: (113 citations)
- 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (35 citations)
- GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Kris M. Kitani focuses on Artificial intelligence, Feature learning, Feature, Pattern recognition and Machine learning.
His Artificial intelligence research is multidisciplinary, relying on both Context and Computer vision.
His research in Context intersects with topics in Representation and Benchmark.
His Human motion and Motion prediction study, which is part of a larger body of work in Computer vision, is frequently linked to Spec# and Sequence, bridging the gap between disciplines.
His study in Feature is interdisciplinary in nature, drawing from both Feature extraction and Feature vector.
His work carried out in the field of Machine learning brings together such families of science as Kalman filter and State.
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