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, Pattern recognition, Segmentation, Pixel and Convolutional neural network.
His research related to Parsing and Point cloud might be considered part of Artificial intelligence.
Peng Wang has included themes like Pascal and Benchmark in his Pattern recognition study.
Peng Wang specializes in Segmentation, namely Image segmentation.
His Pixel study is concerned with the larger field of Computer vision.
His Convolutional neural network study incorporates themes from Inference and Conditional random field.
His most cited work include:
- Towards unified depth and semantic prediction from a single image (337 citations)
- The ApolloScape Dataset for Autonomous Driving (232 citations)
- MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features (180 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Pixel, Pattern recognition, Computer vision and Segmentation.
Convolutional neural network, Object, Image, Image segmentation and Pascal are the primary areas of interest in his Artificial intelligence study.
His Pixel study combines topics in areas such as Depth map, Embedding, Geometry, Normal and Algorithm.
Peng Wang interconnects Inference and Parsing in the investigation of issues within Pattern recognition.
His work in Computer vision addresses subjects such as Deep learning, which are connected to disciplines such as Variation.
His studies in Segmentation integrate themes in fields like Optical flow, Pose, Robustness and Conditional random field.
He most often published in these fields:
- Artificial intelligence (84.21%)
- Pixel (46.05%)
- Pattern recognition (44.74%)
What were the highlights of his more recent work (between 2018-2020)?
- Artificial intelligence (84.21%)
- Pixel (46.05%)
- Computer vision (44.74%)
In recent papers he was focusing on the following fields of study:
Peng Wang focuses on Artificial intelligence, Pixel, Computer vision, Depth map and Convolutional neural network.
His biological study spans a wide range of topics, including Algorithm and Kernel.
His Computer vision research is multidisciplinary, relying on both Translation and Benchmark.
Peng Wang has researched Depth map in several fields, including Single image and Pattern recognition.
His work carried out in the field of Convolutional neural network brings together such families of science as Feature learning and Feature.
His Segmentation research includes elements of Optical flow and Point cloud.
Between 2018 and 2020, his most popular works were:
- The ApolloScape Open Dataset for Autonomous Driving and Its Application (116 citations)
- UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos (68 citations)
- ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving (43 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Pixel, Convolutional neural network, Computer vision and Segmentation.
Particularly relevant to Image segmentation is his body of work in Artificial intelligence.
His Pixel research incorporates themes from Depth map, Algorithm and Pyramid.
His Convolutional neural network research integrates issues from Translation, Rotation and Benchmark.
In general Computer vision, his work in Optical flow and Motion is often linked to Scale and Animation linking many areas of study.
His work deals with themes such as Point cloud and Sensor fusion, which intersect with Segmentation.
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