What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Machine learning, Information retrieval, TRECVID and Search engine indexing.
His Artificial intelligence research integrates issues from Natural language processing, Computer vision and Pattern recognition.
His Computer vision study combines topics from a wide range of disciplines, such as Sampling and Discriminative model.
Cees G. M. Snoek interconnects Classifier, Motion, Key and Encoding in the investigation of issues within Machine learning.
His Information retrieval study combines topics in areas such as Tag cloud and Image retrieval, Semantic gap.
His work carried out in the field of Search engine indexing brings together such families of science as Image processing, Semantics, Multimedia and Lexicon.
His most cited work include:
- Evaluating Color Descriptors for Object and Scene Recognition (1633 citations)
- Early versus late fusion in semantic video analysis (649 citations)
- The challenge problem for automated detection of 101 semantic concepts in multimedia (555 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Information retrieval, TRECVID, Machine learning and Multimedia.
Cees G. M. Snoek works mostly in the field of Artificial intelligence, limiting it down to topics relating to Pattern recognition and, in certain cases, Contextual image classification.
His Information retrieval study incorporates themes from Information visualization and Image retrieval.
His Machine learning research includes themes of Classifier, Categorization and Class.
The Multimedia study combines topics in areas such as Video browsing, The Internet, World Wide Web and Relevance.
His Search engine indexing research is multidisciplinary, incorporating perspectives in Modality and Feature extraction.
He most often published in these fields:
- Artificial intelligence (47.26%)
- Information retrieval (35.27%)
- TRECVID (21.92%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (47.26%)
- Machine learning (20.89%)
- Segmentation (4.79%)
In recent papers he was focusing on the following fields of study:
Cees G. M. Snoek focuses on Artificial intelligence, Machine learning, Segmentation, Computer vision and Embedding.
The study incorporates disciplines such as Class and Pattern recognition in addition to Artificial intelligence.
Cees G. M. Snoek has included themes like Annotation and Isolation in his Machine learning study.
His studies in Computer vision integrate themes in fields like Property and Encoder.
His study in Embedding is interdisciplinary in nature, drawing from both Entropy, Discriminative model and Visualization.
His work deals with themes such as Semantics and Information retrieval, which intersect with Emoji.
Between 2017 and 2021, his most popular works were:
- VideoLSTM convolves, attends and flows for action recognition (221 citations)
- VideoLSTM convolves, attends and flows for action recognition (221 citations)
- Predicting Visual Features From Text for Image and Video Caption Retrieval (86 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Machine learning, Deep learning, Task analysis and Isolation are his primary areas of study.
In most of his Artificial intelligence studies, his work intersects topics such as Pattern recognition.
While the research belongs to areas of Pattern recognition, Cees G. M. Snoek spends his time largely on the problem of Source code, intersecting his research to questions surrounding Embedding.
The various areas that Cees G. M. Snoek examines in his Deep learning study include Annotation and Pascal.
His Isolation research includes elements of Class, Smoothing and Sequence learning.
His Convolutional neural network research incorporates themes from Sentence, Natural language processing, Visualization and Cross-validation.
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