What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Algorithm.
His study in Artificial intelligence focuses on Convolutional neural network, Robustness, Artificial neural network, Segmentation and Training set.
Ian Reid interconnects Video tracking, Structured prediction, Zero shot learning, Benchmark and Generative grammar in the investigation of issues within Pattern recognition.
When carried out as part of a general Machine learning research project, his work on Deep learning and Feature is frequently linked to work in Context model and Social robot, therefore connecting diverse disciplines of study.
His work is dedicated to discovering how Algorithm, Cut are connected with Graph theory and Discrete optimization and other disciplines.
The study incorporates disciplines such as Humanoid robot, Single camera, Visual odometry and Virtual reality in addition to Monocular.
His most cited work include:
- MonoSLAM: Real-Time Single Camera SLAM (2654 citations)
- Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (1201 citations)
- RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation (1131 citations)
What are the main themes of his work throughout his whole career to date?
Ian Reid spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Segmentation.
His is doing research in Deep learning, Object, Convolutional neural network, Tracking and Artificial neural network, both of which are found in Artificial intelligence.
His biological study deals with issues like Benchmark, which deal with fields such as Video tracking.
He combines subjects such as Simultaneous localization and mapping and Affine transformation with his study of Computer vision.
His Pattern recognition study incorporates themes from Contextual image classification, Image and Probabilistic logic.
His biological study spans a wide range of topics, including Pixel and Pascal.
He most often published in these fields:
- Artificial intelligence (85.09%)
- Computer vision (47.15%)
- Pattern recognition (20.61%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (85.09%)
- Computer vision (47.15%)
- Machine learning (16.45%)
In recent papers he was focusing on the following fields of study:
Ian Reid mainly investigates Artificial intelligence, Computer vision, Machine learning, Pattern recognition and Deep learning.
Segmentation, Object, Benchmark, Artificial neural network and Convolutional neural network are the core of his Artificial intelligence study.
Many of his research projects under Computer vision are closely connected to Block with Block, tying the diverse disciplines of science together.
As part of one scientific family, Ian Reid deals mainly with the area of Machine learning, narrowing it down to issues related to the Hidden Markov model, and often Inference.
His research in Pattern recognition intersects with topics in Margin, Image, Image retrieval and Autoencoder.
His work deals with themes such as Ground truth, Noise, Feature learning and Set, which intersect with Deep learning.
Between 2018 and 2021, his most popular works were:
- Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression (320 citations)
- Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks (112 citations)
- Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video (89 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, Machine learning, Deep learning, Computer vision and Segmentation.
Artificial intelligence connects with themes related to Pattern recognition in his study.
His work investigates the relationship between Machine learning and topics such as Sample that intersect with problems in Visualization.
Ian Reid works mostly in the field of Deep learning, limiting it down to topics relating to Feature learning and, in certain cases, Upper and lower bounds, Centroid, Training set, Linearization and Function.
The Iterative reconstruction, Monocular and High-dynamic-range imaging research Ian Reid does as part of his general Computer vision study is frequently linked to other disciplines of science, such as Visibility, therefore creating a link between diverse domains of science.
His research integrates issues of Bundle, Feature, Trajectory and Bundle adjustment in his study of Simultaneous localization and mapping.
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