2022 - Research.com Computer Science in United Arab Emirates Leader Award
2021 - IEEE Fellow For contributions to computer vision and representation learning
2018 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to human action recognition and video understanding
Ling Shao mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Discriminative model. His research links Machine learning with Artificial intelligence. His work deals with themes such as Contextual image classification and Kernel, which intersect with Pattern recognition.
His Computer vision research integrates issues from Field and Salient. His work carried out in the field of Feature extraction brings together such families of science as Feature, Artificial neural network, Speech recognition, Hidden Markov model and Dimensionality reduction. Ling Shao has researched Discriminative model in several fields, including Subspace topology, Embedding, Probabilistic logic and Robustness.
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Discriminative model. His work in Segmentation, Feature extraction, Deep learning, Feature and Image are all subfields of Artificial intelligence research. His work carried out in the field of Pattern recognition brings together such families of science as Contextual image classification, Hash function and Robustness.
The Hash function study combines topics in areas such as Binary code and Image retrieval. His biological study spans a wide range of topics, including Salient and Detector. His study in Machine learning is interdisciplinary in nature, drawing from both Visualization, Representation, Inference and Training set.
Ling Shao spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Segmentation and Discriminative model. His study looks at the relationship between Artificial intelligence and topics such as Computer vision, which overlap with Artificial neural network. His study looks at the intersection of Pattern recognition and topics like Embedding with Class.
As a part of the same scientific study, he usually deals with the Machine learning, concentrating on Code and frequently concerns with Algorithm. The various areas that Ling Shao examines in his Object study include Field and Salient. His work deals with themes such as Weighting, Pedestrian detection and Feature learning, which intersect with Feature.
Ling Shao focuses on Artificial intelligence, Pattern recognition, Segmentation, Discriminative model and Machine learning. The study of Artificial intelligence is intertwined with the study of Computer vision in a number of ways. His Pattern recognition research is multidisciplinary, incorporating elements of Embedding, Face, Contextual image classification, Channel and Benchmark.
His Segmentation study which covers Object detection that intersects with Pyramid, Speedup and Feature extraction. His Discriminative model study combines topics from a wide range of disciplines, such as Semantics, Linear subspace and Visualization. His research integrates issues of Image resolution and Inference in his study of Machine learning.
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Enhanced Computer Vision With Microsoft Kinect Sensor: A Review
Jungong Han;Ling Shao;Dong Xu;Jamie Shotton.
IEEE Transactions on Systems, Man, and Cybernetics (2013)
A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior
Qingsong Zhu;Jiaming Mai;Ling Shao.
IEEE Transactions on Image Processing (2015)
A survey on fall detection: Principles and approaches
Muhammad Mubashir;Ling Shao;Luke Seed.
Recent advances and trends in visual tracking: A review
Hanxuan Yang;Ling Shao;Feng Zheng;Liang Wang.
A rapid learning algorithm for vehicle classification
Xuezhi Wen;Ling Shao;Yu Xue;Wei Fang.
Information Sciences (2015)
Video Salient Object Detection via Fully Convolutional Networks
Wenguan Wang;Jianbing Shen;Ling Shao.
IEEE Transactions on Image Processing (2018)
Design and performance evaluation of a whole-body Ingenuity TF PET-MRI system
Habib Zaidi;N Ojha;M Morich;J Griesmer.
Physics in Medicine and Biology (2011)
Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition
Di Wu;Lionel Pigou;Pieter-Jan Kindermans;Nam Do-Hoang Le.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2016)
Transfer Learning for Visual Categorization: A Survey
Ling Shao;Fan Zhu;Xuelong Li.
IEEE Transactions on Neural Networks (2015)
From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms
Ling Shao;Ruomei Yan;Xuelong Li;Yan Liu.
IEEE Transactions on Systems, Man, and Cybernetics (2014)
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