What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Computer vision, Benchmark, Convolutional neural network and Pattern recognition.
Many of his studies on Artificial intelligence apply to Deconvolution as well.
He has included themes like Geodesic and Synthetic data in his Computer vision study.
Yuchao Dai combines subjects such as RGB color model, Point cloud, Image and Metric with his study of Benchmark.
His Convolutional neural network research includes themes of Margin, Translation, Depth map and Color image.
The Pattern recognition study combines topics in areas such as Pixel, Ranking, Image sensor and Normal.
His most cited work include:
- NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results (555 citations)
- Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs (399 citations)
- A Simple Prior-Free Method for Non-rigid Structure-from-Motion Factorization (211 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Benchmark and Convolutional neural network.
His Artificial intelligence study focuses mostly on Image, Object detection, Depth map, Feature extraction and Feature.
His research on Computer vision frequently links to adjacent areas such as Motion.
His study looks at the relationship between Pattern recognition and fields such as RGB color model, as well as how they intersect with chemical problems.
His Benchmark research includes elements of Object, Saliency map, Metric and Salience.
His Convolutional neural network study combines topics in areas such as Margin, Artificial neural network and Inference.
He most often published in these fields:
- Artificial intelligence (80.38%)
- Computer vision (52.53%)
- Pattern recognition (25.95%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (80.38%)
- Benchmark (20.89%)
- Pattern recognition (25.95%)
In recent papers he was focusing on the following fields of study:
Yuchao Dai mainly focuses on Artificial intelligence, Benchmark, Pattern recognition, Computer vision and Depth map.
In his articles, Yuchao Dai combines various disciplines, including Artificial intelligence and Process.
Yuchao Dai has researched Benchmark in several fields, including Metric and Salience.
His Pattern recognition study combines topics in areas such as Object and RGB color model.
In general Computer vision, his work in View synthesis, Rendering, 3D reconstruction and Optical flow is often linked to Robotics linking many areas of study.
Yuchao Dai combines subjects such as Regularization, Algorithm, Pose and Deep learning with his study of Depth map.
Between 2019 and 2021, his most popular works were:
- UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders (68 citations)
- Weakly-Supervised Salient Object Detection via Scribble Annotations (29 citations)
- Hierarchical Neural Architecture Search for Deep Stereo Matching (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of study are Artificial intelligence, Benchmark, Pattern recognition, Segmentation and Pipeline.
He regularly links together related areas like Computer vision in his Artificial intelligence studies.
His Computer vision research incorporates elements of Planar, Sequence and Surface.
His Benchmark research includes elements of Probabilistic logic and Inference.
The various areas that Yuchao Dai examines in his Pattern recognition study include Latent variable, Object, Object detection, Salience and Saliency map.
His Segmentation research incorporates themes from Feature extraction, Filter and Feature.
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