What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Yannis Avrithis mainly focuses on Artificial intelligence, Pattern recognition, Image retrieval, Information retrieval and Computer vision.
Search engine indexing, Artificial neural network, Deep learning, Categorization and Visualization are among the areas of Artificial intelligence where Yannis Avrithis concentrates his study.
Yannis Avrithis interconnects Image processing and Contextual image classification in the investigation of issues within Pattern recognition.
His work deals with themes such as Scalability, Query expansion, Kernel and Landmark, which intersect with Image retrieval.
His biological study spans a wide range of topics, including Annotation and Personalization.
His biological study deals with issues like Feature vector, which deal with fields such as Multiview Video Coding and Key frame.
His most cited work include:
- Locally Optimized Product Quantization for Approximate Nearest Neighbor Search (199 citations)
- Semantic annotation of images and videos for multimedia analysis (187 citations)
- To Aggregate or Not to aggregate: Selective Match Kernels for Image Search (168 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Image retrieval and Multimedia.
As a part of the same scientific family, Yannis Avrithis mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Class.
His Pattern recognition research includes elements of Object, Representation and Feature detection.
His Image retrieval research incorporates elements of Query expansion and Convolutional neural network.
Yannis Avrithis focuses mostly in the field of Multimedia, narrowing it down to topics relating to Information retrieval and, in certain cases, Semantics, Annotation, Knowledge representation and reasoning and Natural language processing.
The Feature extraction study which covers Thesaurus that intersects with Cluster analysis.
He most often published in these fields:
- Artificial intelligence (60.62%)
- Pattern recognition (34.72%)
- Computer vision (23.83%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (60.62%)
- Pattern recognition (34.72%)
- Image retrieval (21.24%)
In recent papers he was focusing on the following fields of study:
Yannis Avrithis mostly deals with Artificial intelligence, Pattern recognition, Image retrieval, Graph and Machine learning.
Many of his studies on Artificial intelligence apply to Manifold as well.
The Pattern recognition study combines topics in areas such as Kernel, Image, Representation and Detector.
His studies examine the connections between Representation and genetics, as well as such issues in Metric, with regards to Cluster analysis, Visual search and Information retrieval.
His Image retrieval study contributes to a more complete understanding of Computer vision.
His study in the field of Panorama and Color image also crosses realms of Semantic memory.
Between 2015 and 2021, his most popular works were:
- Label Propagation for Deep Semi-Supervised Learning (146 citations)
- Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations (112 citations)
- Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking (106 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His primary scientific interests are in Artificial intelligence, Pattern recognition, Image retrieval, Manifold and Metric.
His Artificial intelligence study typically links adjacent topics like Query expansion.
His work on Pattern recognition is typically connected to Graph as part of general Pattern recognition study, connecting several disciplines of science.
The concepts of his Image retrieval study are interwoven with issues in Annotation, Information retrieval and Benchmark.
He has included themes like Artificial neural network and Nearest neighbor graph in his Manifold study.
His study in Computer vision is interdisciplinary in nature, drawing from both Search engine indexing and Feature vector.
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