What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Andrea Vedaldi focuses on Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Machine learning.
Her study in Artificial neural network, Object detection, Visualization, Contextual image classification and Object is carried out as part of her Artificial intelligence studies.
Her Pattern recognition research is multidisciplinary, incorporating perspectives in Image and Representation.
Her work on Video tracking, Image processing and Bag-of-words model in computer vision as part of general Computer vision study is frequently linked to Automation, bridging the gap between disciplines.
Her biological study spans a wide range of topics, including Scale-invariant feature transform, Spotting and Image texture.
Her Machine learning research integrates issues from Residual and Image retrieval.
Her most cited work include:
- Deep face recognition (3327 citations)
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps (2838 citations)
- Vlfeat: an open and portable library of computer vision algorithms (2551 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Object.
Her study in Artificial neural network, Convolutional neural network, Image, Deep learning and Contextual image classification is done as part of Artificial intelligence.
Her Pattern recognition study incorporates themes from Pixel, Object detection and Representation.
Computer vision and Invariant are frequently intertwined in her study.
Her Machine learning research incorporates themes from Training set and Benchmark.
Andrea Vedaldi combines subjects such as Structure, Probabilistic logic, Unsupervised learning and Task with her study of Object.
She most often published in these fields:
- Artificial intelligence (86.74%)
- Pattern recognition (41.94%)
- Computer vision (23.30%)
What were the highlights of her more recent work (between 2019-2021)?
- Artificial intelligence (86.74%)
- Pattern recognition (41.94%)
- Object (20.79%)
In recent papers she was focusing on the following fields of study:
Andrea Vedaldi mainly focuses on Artificial intelligence, Pattern recognition, Object, Machine learning and Image.
Her work on Computer vision expands to the thematically related Artificial intelligence.
Her work carried out in the field of Computer vision brings together such families of science as Robot and Task.
The Pattern recognition study combines topics in areas such as Ambiguous image, 3D reconstruction, Filter and Code.
Her study in Machine learning is interdisciplinary in nature, drawing from both Age prediction and Benchmark.
Andrea Vedaldi interconnects Adversarial system, Unsupervised learning, Consistency and Training set in the investigation of issues within Image.
Between 2019 and 2021, her most popular works were:
- Self-labelling via simultaneous clustering and representation learning (90 citations)
- Unsupervised Learning of Probably Symmetric Deformable 3D Objects From Images in the Wild (46 citations)
- Fixing the train-test resolution discrepancy: FixEfficientNet (46 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Andrea Vedaldi mostly deals with Artificial intelligence, Pattern recognition, Image, Machine learning and Leverage.
All of her Artificial intelligence and Convolutional neural network, Deep learning, Cluster analysis, Iterative reconstruction and Object investigations are sub-components of the entire Artificial intelligence study.
Andrea Vedaldi mostly deals with Feature extraction in her studies of Pattern recognition.
The various areas that Andrea Vedaldi examines in her Image study include Test, Training set, Unsupervised learning, Feature learning and Resolution.
In general Machine learning study, her work on Transfer of learning often relates to the realm of Quality and Network architecture, thereby connecting several areas of interest.
Her studies in Leverage integrate themes in fields like Semi-supervised learning, Representation, External Data Representation and Overfitting.
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