What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Fisher Yu mainly investigates Artificial intelligence, Pattern recognition, Segmentation, Machine learning and Contextual image classification.
His work investigates the relationship between Artificial intelligence and topics such as Computer vision that intersect with problems in Leverage.
His study looks at the relationship between Pattern recognition and topics such as Deep learning, which overlap with 3D single-object recognition, Active shape model and Cognitive neuroscience of visual object recognition.
In general Segmentation study, his work on Image segmentation often relates to the realm of Scalability, thereby connecting several areas of interest.
His work deals with themes such as Visualization, Training set, State and Motion, which intersect with Machine learning.
His Contextual image classification research incorporates themes from Image resolution, Resolution and Adaptation.
His most cited work include:
- Multi-Scale Context Aggregation by Dilated Convolutions (3429 citations)
- 3D ShapeNets: A deep representation for volumetric shapes (2166 citations)
- ShapeNet: An Information-Rich 3D Model Repository (1726 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Machine learning, Computer vision, Pattern recognition and Segmentation are his primary areas of study.
Fisher Yu undertakes interdisciplinary study in the fields of Artificial intelligence and Construct through his research.
His Machine learning research integrates issues from Training set, Embedding, Inference, State and Benchmark.
His study in the field of Tracking, Object and Voxel is also linked to topics like Pipeline and Sketch.
Fisher Yu has included themes like Deep learning and Feature generation in his Pattern recognition study.
In general Segmentation, his work in Image segmentation is often linked to Scale linking many areas of study.
He most often published in these fields:
- Artificial intelligence (89.86%)
- Machine learning (30.43%)
- Computer vision (28.99%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (89.86%)
- Object detection (10.14%)
- Pattern recognition (24.64%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Object detection, Pattern recognition, Machine learning and Computer vision.
His study in Artificial intelligence concentrates on Object, Video tracking, Pascal, Image segmentation and Segmentation.
His Segmentation research is multidisciplinary, incorporating perspectives in Pixel, Feature learning and Natural language processing.
His Pattern recognition study frequently links to adjacent areas such as Real image.
In most of his Machine learning studies, his work intersects topics such as State.
His Computer vision research includes elements of Principle of maximum entropy, Task and Robustness.
Between 2019 and 2021, his most popular works were:
- BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning (99 citations)
- Frustratingly Simple Few-Shot Object Detection (42 citations)
- Exploring Cross-Image Pixel Contrast for Semantic Segmentation (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His primary scientific interests are in Artificial intelligence, Computer vision, Shot, Simple and Object detection.
Image segmentation and Segmentation are the core of his Artificial intelligence study.
His Image segmentation research is multidisciplinary, relying on both Visualization and Machine learning.
His Segmentation study combines topics in areas such as Pixel and Natural language processing.
His Overhead investigation overlaps with Feature learning and Focus.
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