What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Object detection and Machine learning.
While working in this field, Nuno Vasconcelos studies both Artificial intelligence and Detector.
His work on Discriminant and Classifier is typically connected to Fisher kernel and Gaussian process as part of general Pattern recognition study, connecting several disciplines of science.
His work carried out in the field of Computer vision brings together such families of science as Salient and Salience.
His research in Object detection intersects with topics in Artificial neural network, Cognitive neuroscience of visual object recognition and Feature.
His work on LogitBoost and Feature as part of general Machine learning study is frequently linked to Sample and Facial expression recognition, therefore connecting diverse disciplines of science.
His most cited work include:
- Cascade R-CNN: Delving Into High Quality Object Detection (1077 citations)
- A new approach to cross-modal multimedia retrieval (921 citations)
- Anomaly detection in crowded scenes (883 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Image retrieval.
His studies in Object detection, Feature extraction, Cognitive neuroscience of visual object recognition, Contextual image classification and Image segmentation are all subfields of Artificial intelligence research.
Nuno Vasconcelos has researched Cognitive neuroscience of visual object recognition in several fields, including Artificial neural network and Inference.
His Pattern recognition study integrates concerns from other disciplines, such as Kadir–Brady saliency detector, Image and Robustness.
His Machine learning study combines topics from a wide range of disciplines, such as Classifier and Bayes' theorem.
The study incorporates disciplines such as Divergence-from-randomness model, Information retrieval and Data mining in addition to Image retrieval.
He most often published in these fields:
- Artificial intelligence (81.65%)
- Pattern recognition (41.95%)
- Computer vision (29.21%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (81.65%)
- Machine learning (22.47%)
- Pattern recognition (41.95%)
In recent papers he was focusing on the following fields of study:
Nuno Vasconcelos mainly investigates Artificial intelligence, Machine learning, Pattern recognition, Deep learning and Classifier.
In his study, Identification is strongly linked to Computer vision, which falls under the umbrella field of Artificial intelligence.
His Machine learning research is multidisciplinary, incorporating elements of Entropy, Entropy and Embedding.
His Pattern recognition study combines topics in areas such as Fisher vector, Feature and Statistical model.
His Deep learning research includes themes of Feature learning, Transfer of learning, Voxel, Convolutional neural network and Data set.
The various areas that he examines in his Classifier study include Discriminant, Optimization problem and Stochastic gradient descent.
Between 2018 and 2021, his most popular works were:
- Bidirectional Learning for Domain Adaptation of Semantic Segmentation (114 citations)
- Cascade R-CNN: High Quality Object Detection and Instance Segmentation (103 citations)
- REPAIR: Removing Representation Bias by Dataset Resampling (58 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Artificial intelligence, Deep learning, Detector, Object detection and Pattern recognition are his primary areas of study.
His Artificial intelligence study combines topics in areas such as Machine learning and Adaptation.
His study in Deep learning is interdisciplinary in nature, drawing from both Transfer of learning, Algorithm, Feature learning and Convolutional neural network.
His study on Object detection is mostly dedicated to connecting different topics, such as Overfitting.
His research integrates issues of Stochastic gradient descent, Optimization problem and Resampling in his study of Pattern recognition.
His Computer vision research is multidisciplinary, incorporating elements of Training set, Benchmark and Code.
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