What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
Jiaya Jia mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Image and Image segmentation.
His study in Segmentation, Deblurring, Image restoration, Artificial neural network and Feature extraction is done as part of Artificial intelligence.
His Computer vision research is multidisciplinary, incorporating perspectives in Iterative method and Algorithm.
His research in Pattern recognition intersects with topics in Blind deconvolution and Benchmark.
His research in the fields of Upsampling overlaps with other disciplines such as Boundary, Coherence, Display resolution and Structure.
Jiaya Jia combines subjects such as Pixel and Pascal with his study of Image segmentation.
His most cited work include:
- Pyramid Scene Parsing Network (3766 citations)
- High-quality motion deblurring from a single image (1144 citations)
- Hierarchical Saliency Detection (1123 citations)
What are the main themes of his work throughout his whole career to date?
Jiaya Jia focuses on Artificial intelligence, Computer vision, Image, Pattern recognition and Segmentation.
His study in Artificial intelligence concentrates on Pixel, Feature, Feature extraction, Object and Object detection.
His work in Object detection covers topics such as Machine learning which are related to areas like Training set.
Image restoration, Image processing, Image segmentation, Motion estimation and Deblurring are the core of his Computer vision study.
Jiaya Jia interconnects Artificial neural network and Algorithm in the investigation of issues within Image.
His Segmentation research includes themes of Embedding and Inference.
He most often published in these fields:
- Artificial intelligence (91.24%)
- Computer vision (58.39%)
- Image (29.56%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (91.24%)
- Computer vision (58.39%)
- Segmentation (22.99%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Computer vision, Segmentation, Image and Pattern recognition.
His Artificial intelligence study integrates concerns from other disciplines, such as Point and Machine learning.
His study in the field of Feature extraction and Voxel also crosses realms of Networking hardware.
The concepts of his Segmentation study are interwoven with issues in Theoretical computer science, Boosting, Benchmark, Kernel and Robustness.
He usually deals with Image and limits it to topics linked to Translation and Interpolation.
His research integrates issues of Artificial neural network, Inpainting and Normalization in his study of Pattern recognition.
Between 2019 and 2021, his most popular works were:
- Exploring Self-Attention for Image Recognition (68 citations)
- 3DSSD: Point-Based 3D Single Stage Object Detector (60 citations)
- PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
Jiaya Jia mainly investigates Artificial intelligence, Computer vision, Segmentation, Object detection and Image.
His work on Feature extraction is typically connected to Generalization as part of general Artificial intelligence study, connecting several disciplines of science.
His Computer vision research incorporates elements of Self attention and Robustness.
He focuses mostly in the field of Segmentation, narrowing it down to matters related to Machine learning and, in some cases, Variety.
His research investigates the link between Object detection and topics such as Margin that cross with problems in Upsampling.
His Deblurring and Image processing study in the realm of Image connects with subjects such as Terminal device and Carry.
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