What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Artificial intelligence, Computer vision, Benchmark, Tracking and Video tracking are his primary areas of study.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
He has included themes like Computational complexity theory and Representation in his Computer vision study.
His Benchmark course of study focuses on Deep learning and Variety.
His studies deal with areas such as Algorithm and Sparse approximation as well as Tracking.
His study in Video tracking is interdisciplinary in nature, drawing from both Ground truth, Eye tracking and Pattern recognition.
His most cited work include:
- ActivityNet: A large-scale video benchmark for human activity understanding (1057 citations)
- Robust visual tracking via multi-task sparse learning (576 citations)
- A Benchmark and Simulator for UAV Tracking (562 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Robustness.
In general Artificial intelligence study, his work on Video tracking, Object detection, Deep learning and Benchmark often relates to the realm of Action, thereby connecting several areas of interest.
His research in Video tracking intersects with topics in Particle filter and Eye tracking.
His Machine learning study often links to related topics such as Question answering.
His biological study deals with issues like Algorithm, which deal with fields such as Nonlinear system.
His Tracking research focuses on subjects like Sparse approximation, which are linked to Representation.
He most often published in these fields:
- Artificial intelligence (70.71%)
- Computer vision (21.76%)
- Pattern recognition (19.67%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (70.71%)
- Pattern recognition (19.67%)
- Point cloud (9.21%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Point cloud, Segmentation and Theoretical computer science.
As a member of one scientific family, Bernard Ghanem mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Robustness.
The Pattern recognition study combines topics in areas such as Generator, Image, Face and Task.
As part of one scientific family, Bernard Ghanem deals mainly with the area of Point cloud, narrowing it down to issues related to the Distributed computing, and often Robot.
His Segmentation research incorporates themes from Kernel and Benchmark.
Bernard Ghanem interconnects Affine transformation, Feature learning, Convex hull and Graph in the investigation of issues within Theoretical computer science.
Between 2019 and 2021, his most popular works were:
- DeeperGCN: All You Need to Train Deeper GCNs (42 citations)
- G-TAD: Sub-Graph Localization for Temporal Action Detection (41 citations)
- SGAS: Sequential Greedy Architecture Search (35 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of study are Artificial intelligence, Graph, Theoretical computer science, Adversarial system and Feature extraction.
His biological study spans a wide range of topics, including Machine learning and Pattern recognition.
His Machine learning research includes elements of Modality and Correlation.
His Graph study combines topics in areas such as Convolutional neural network and Feature vector.
His Adversarial system research is multidisciplinary, relying on both Distributed computing and Robustness.
His work focuses on many connections between Feature extraction and other disciplines, such as Image resolution, that overlap with his field of interest in Pixel.
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