What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Hash function and Theoretical computer science.
Wei Liu studied Artificial intelligence and Computer vision that intersect with Resolution.
The study incorporates disciplines such as Modal, Feature, Contextual image classification, Image and Supervised learning in addition to Pattern recognition.
His Machine learning research includes themes of Training set and Robustness.
His Hash function research is multidisciplinary, incorporating elements of Hamming space, Deep learning and Search engine indexing.
His Theoretical computer science research incorporates themes from Feature hashing, Nearest neighbor search, Universal hashing and Dynamic perfect hashing.
His most cited work include:
- Supervised hashing with kernels (1111 citations)
- Hashing with Graphs (848 citations)
- SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning (731 citations)
What are the main themes of his work throughout his whole career to date?
Wei Liu focuses on Artificial intelligence, Computer vision, Pattern recognition, Image and Machine learning.
His studies examine the connections between Artificial intelligence and genetics, as well as such issues in Natural language processing, with regards to Word.
His study in the field of Tracking and Object also crosses realms of Field and Process.
Wei Liu does research in Pattern recognition, focusing on Feature extraction specifically.
His Machine learning research integrates issues from Hash function, Image retrieval, Metric and Robustness.
His research in Double hashing, Universal hashing, Dynamic perfect hashing and Feature hashing are components of Hash function.
He most often published in these fields:
- Artificial intelligence (61.35%)
- Computer vision (19.25%)
- Pattern recognition (18.50%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (61.35%)
- Computer vision (19.25%)
- Image (15.04%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Computer vision, Image, Deep learning and Machine learning.
In his research on the topic of Artificial intelligence, Similarity is strongly related with Pattern recognition.
His work on Augmented reality, Face, Object and Tracking as part of general Computer vision research is frequently linked to Process, bridging the gap between disciplines.
The Image study which covers Sample that intersects with Function.
Wei Liu studies Machine learning, focusing on Discriminative model in particular.
Wei Liu interconnects Language model, Natural language, Natural language processing and Metric in the investigation of issues within Closed captioning.
Between 2019 and 2021, his most popular works were:
- Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm (152 citations)
- Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression (117 citations)
- Graph Convolutional Network Hashing (39 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Wei Liu mainly focuses on Artificial intelligence, Algorithm, Deep learning, Pattern recognition and Applied mathematics.
The Artificial intelligence study combines topics in areas such as Machine learning and Computer vision.
His Algorithm study integrates concerns from other disciplines, such as Shearing, Filter bank, Image fusion and Spatial frequency.
His Deep learning research is multidisciplinary, relying on both Object detection, Minimum bounding box, Inference, Cluster analysis and Point.
Wei Liu studies Pattern recognition, namely Convolutional neural network.
The various areas that Wei Liu examines in his Applied mathematics study include Gradient descent, Convex function and Smoothness.
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