What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Object and Segmentation.
Many of his research projects under Artificial intelligence are closely connected to Storyboard with Storyboard, tying the diverse disciplines of science together.
His biological study spans a wide range of topics, including TRACE and Pattern recognition.
His Pattern recognition study combines topics in areas such as Optical flow, Pairwise comparison and Complete graph.
His work carried out in the field of Object brings together such families of science as Shadow, Similarity and Computer graphics.
In the subject of general Segmentation, his work in Scale-space segmentation is often linked to Set, thereby combining diverse domains of study.
His most cited work include:
- Discovering important people and objects for egocentric video summarization (583 citations)
- Key-segments for video object segmentation (412 citations)
- Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-Supervised Object and Action Localization (236 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Computer vision, Image and Object are his primary areas of study.
His study brings together the fields of Machine learning and Artificial intelligence.
His Pattern recognition study incorporates themes from Real image, Pairwise comparison, Generative model and Image generation.
His work on Feature, Image warping and Image segmentation as part of general Computer vision study is frequently connected to Frame, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Image segmentation research integrates issues from Pixel and Feature extraction.
The concepts of his Image study are interwoven with issues in Social media, Contrast and Social network.
He most often published in these fields:
- Artificial intelligence (80.46%)
- Pattern recognition (35.63%)
- Computer vision (28.74%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (80.46%)
- Pattern recognition (35.63%)
- Segmentation (13.79%)
In recent papers he was focusing on the following fields of study:
Yong Jae Lee spends much of his time researching Artificial intelligence, Pattern recognition, Segmentation, Feature and Convolution.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Code.
His Pattern recognition research incorporates themes from Image generation and Generative model.
His Feature research is classified as research in Computer vision.
Yong Jae Lee studies Optical flow which is a part of Computer vision.
His research in Discriminative model intersects with topics in Context, Object detection, Ground truth and Benchmark.
Between 2019 and 2021, his most popular works were:
- YOLACT++: Better Real-time Instance Segmentation. (28 citations)
- Audiovisual SlowFast Networks for Video Recognition (22 citations)
- Don’t Judge an Object by Its Context: Learning to Overcome Contextual Bias (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Yong Jae Lee mainly focuses on Artificial intelligence, Machine learning, Leverage, Code and Context.
His research integrates issues of Algorithm and Context model in his study of Artificial intelligence.
His Machine learning research includes elements of Visualization and Robustness.
His study in Leverage is interdisciplinary in nature, drawing from both Real image, Image generation, Generative model and Pattern recognition.
His studies deal with areas such as Regularization and Joint as well as Code.
His work deals with themes such as Object, Object detection, Benchmark, Ground truth and Discriminative model, which intersect with Context.
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