What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Liang-Chieh Chen mainly investigates Artificial intelligence, Pattern recognition, Convolutional neural network, Image segmentation and Segmentation.
Liang-Chieh Chen combines subjects such as Pyramid, Pascal and Test set with his study of Pattern recognition.
Liang-Chieh Chen works mostly in the field of Pyramid, limiting it down to topics relating to Convolution and, in certain cases, Deep learning and Upsampling.
The Convolutional neural network study combines topics in areas such as Semantic image segmentation, CRFS, Conditional random field, Scale-space segmentation and Graphical model.
His Image segmentation research focuses on subjects like Object detection, which are linked to Algorithm and Artificial neural network.
His Segmentation research focuses on subjects like Pooling, which are linked to Separable space, Mobile phone and Search algorithm.
His most cited work include:
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs (6128 citations)
- MobileNetV2: Inverted Residuals and Linear Bottlenecks (3526 citations)
- Rethinking Atrous Convolution for Semantic Image Segmentation (2281 citations)
What are the main themes of his work throughout his whole career to date?
Liang-Chieh Chen focuses on Artificial intelligence, Segmentation, Pattern recognition, Computer vision and Image segmentation.
His Artificial intelligence study frequently draws connections between related disciplines such as Machine learning.
His Segmentation study combines topics in areas such as Object, Object detection, Contextual image classification, Algorithm and Discriminative model.
His Pattern recognition research incorporates themes from Pixel and Parsing.
Liang-Chieh Chen studied Computer vision and Embedding that intersect with Probabilistic logic and Task.
His work carried out in the field of Convolutional neural network brings together such families of science as Convolution and Scale-space segmentation.
He most often published in these fields:
- Artificial intelligence (80.82%)
- Segmentation (57.53%)
- Pattern recognition (53.42%)
What were the highlights of his more recent work (between 2019-2021)?
- Segmentation (57.53%)
- Artificial intelligence (80.82%)
- Computer vision (23.29%)
In recent papers he was focusing on the following fields of study:
Segmentation, Artificial intelligence, Computer vision, Test set and Image segmentation are his primary areas of study.
His study in Segmentation is interdisciplinary in nature, drawing from both Closing, Algorithm and Convolution.
His study connects Pattern recognition and Artificial intelligence.
His Pattern recognition study incorporates themes from Image resolution, Frame rate, Margin and Image prediction.
The study incorporates disciplines such as Embedding, Probabilistic logic, Metric and Benchmark in addition to Computer vision.
In his study, which falls under the umbrella issue of Test set, Leverage is strongly linked to Discriminative model.
Between 2019 and 2021, his most popular works were:
- Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation (64 citations)
- DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution (49 citations)
- Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation (39 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Liang-Chieh Chen mostly deals with Segmentation, Algorithm, Convolution, Artificial intelligence and Range.
His biological study spans a wide range of topics, including Image resolution and Pyramid.
His studies deal with areas such as Image, Image segmentation, Margin, Frame rate and Feature extraction as well as Image resolution.
His Image segmentation study improves the overall literature in Pattern recognition.
His Pyramid research integrates issues from Object, Computer vision and Pyramid.
His studies in Range integrate themes in fields like Contextual image classification and Block.
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