What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Ali Borji mostly deals with Artificial intelligence, Pattern recognition, Salience, Segmentation and Object detection.
His Artificial intelligence research integrates issues from Machine learning and Computer vision.
His Pattern recognition study integrates concerns from other disciplines, such as Object and Saliency map.
The study incorporates disciplines such as Salient, Visual saliency, Visual attention and Eye movement in addition to Salience.
His work is dedicated to discovering how Object detection, Artificial neural network are connected with Fixation and other disciplines.
His research investigates the connection between Visualization and topics such as Kadir–Brady saliency detector that intersect with issues in Data science.
His most cited work include:
- State-of-the-Art in Visual Attention Modeling (1367 citations)
- Salient Object Detection: A Benchmark (905 citations)
- Deeply Supervised Salient Object Detection with Short Connections (516 citations)
What are the main themes of his work throughout his whole career to date?
Ali Borji mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Salience and Machine learning.
His study in Object, Visualization, Segmentation, Cognitive neuroscience of visual object recognition and Convolutional neural network is carried out as part of his studies in Artificial intelligence.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Object detection, Image and Deep learning.
His work on Gaze and Feature as part of his general Computer vision study is frequently connected to Natural and Graph, thereby bridging the divide between different branches of science.
His research integrates issues of Salient, Visual saliency, Visual attention, Eye movement and Benchmark in his study of Salience.
Ali Borji has researched Machine learning in several fields, including Adversarial system and Automatic summarization.
He most often published in these fields:
- Artificial intelligence (86.56%)
- Pattern recognition (34.41%)
- Computer vision (32.26%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (86.56%)
- Pattern recognition (34.41%)
- Deep learning (10.75%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Deep learning, Benchmark and Computer vision.
His research combines Machine learning and Artificial intelligence.
His research investigates the connection with Pattern recognition and areas like Channel which intersect with concerns in Leverage, Salient object detection, Depth perception and Pyramid.
His studies deal with areas such as Computational neuroscience, Object detection, Recurrent neural network and Perception as well as Deep learning.
His work in Computer vision addresses issues such as Salient, which are connected to fields such as Transformation, Eye movement and Gaze.
Ali Borji works mostly in the field of Visualization, limiting it down to concerns involving Visual saliency and, occasionally, Visual attention and Data science.
Between 2019 and 2021, his most popular works were:
- Revisiting Video Saliency Prediction in the Deep Learning Era (92 citations)
- Inferring Salient Objects from Human Fixations (62 citations)
- Saliency Prediction in the Deep Learning Era: Successes and Limitations (49 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Ali Borji spends much of his time researching Artificial intelligence, Salience, Pattern recognition, Visualization and Deep learning.
As part of his studies on Artificial intelligence, Ali Borji often connects relevant areas like Visual attention.
His studies in Visual attention integrate themes in fields like Segmentation, Eye tracking, Computer vision, Overfitting and Object detection.
His Visualization study integrates concerns from other disciplines, such as Transformation, Gaze, Salient and Eye movement.
His Deep learning research incorporates themes from Visual saliency, Data science and Salient objects.
His Benchmark research integrates issues from Pooling, Feedforward neural network, Feature, Pyramid and Convolutional neural network.
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