What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Facial recognition system and Deep learning.
His Artificial intelligence research includes elements of Machine learning and Detector.
His study in the field of Histogram is also linked to topics like Action recognition.
His research brings together the fields of Image and Pattern recognition.
His Three-dimensional face recognition study in the realm of Facial recognition system interacts with subjects such as Expression.
As a member of one scientific family, Ajmal Mian mostly works in the field of Deep learning, focusing on Adversarial system and, on occasion, Classifier and Discrete cosine transform.
His most cited work include:
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey (699 citations)
- An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition (421 citations)
- Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes (393 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Facial recognition system and Deep learning.
Artificial intelligence and Machine learning are frequently intertwined in his study.
The study incorporates disciplines such as Histogram and Image in addition to Pattern recognition.
His work carried out in the field of Computer vision brings together such families of science as Feature and Hash table.
He interconnects Subspace topology and Facial expression in the investigation of issues within Facial recognition system.
The concepts of his Deep learning study are interwoven with issues in Adversarial system, Segmentation, Artificial neural network and Object, Object detection.
He most often published in these fields:
- Artificial intelligence (79.04%)
- Pattern recognition (44.85%)
- Computer vision (38.97%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (79.04%)
- Deep learning (16.91%)
- Machine learning (9.19%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Deep learning, Machine learning, Pattern recognition and Object.
In his study, Leverage is inextricably linked to Computer vision, which falls within the broad field of Artificial intelligence.
His work deals with themes such as Adversarial system, Object detection, Feature extraction and Benchmark, which intersect with Deep learning.
His Adversarial system study deals with Image intersecting with Hyperspectral imaging, Smoothness and Quantization.
His work in the fields of Machine learning, such as Self supervised learning and Artificial neural network, intersects with other areas such as Diffusion and Maximization.
Ajmal Mian has researched Pattern recognition in several fields, including RGB color model, Visualization, Inpainting and Salient.
Between 2019 and 2021, his most popular works were:
- Deep Affinity Network for Multiple Object Tracking (70 citations)
- Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system (30 citations)
- Target-aware Holistic Influence Maximization in Spatial Social Networks (28 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Ajmal Mian mainly focuses on Artificial intelligence, Deep learning, Object, Pattern recognition and Point cloud.
His Artificial intelligence research incorporates themes from Machine learning and Simulation.
His studies deal with areas such as Artificial neural network, Object detection and Ground truth as well as Deep learning.
His Object study is concerned with the field of Computer vision as a whole.
His work on Video tracking is typically connected to Intersection as part of general Computer vision study, connecting several disciplines of science.
The study incorporates disciplines such as RGB color model, Quantization and Reflectivity in addition to Pattern recognition.
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