What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Josef Sivic mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Image retrieval and Visualization.
His Artificial intelligence study typically links adjacent topics like Natural language processing.
In general Computer vision, his work in Matching, Crowd density and Person detection is often linked to Principal linking many areas of study.
His Pattern recognition study combines topics from a wide range of disciplines, such as Object, Cognitive neuroscience of visual object recognition, Representation and Contextual image classification.
His work in Image retrieval addresses issues such as Information retrieval, which are connected to fields such as Ranking and Quantization.
The study incorporates disciplines such as Augmented reality, Pose and Ground truth in addition to Visualization.
His most cited work include:
- Object retrieval with large vocabularies and fast spatial matching (2554 citations)
- Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks (2190 citations)
- Lost in quantization: Improving particular object retrieval in large scale image databases (1270 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Object and Natural language processing.
Artificial intelligence is closely attributed to Machine learning in his research.
His research in Pattern recognition intersects with topics in Latent Dirichlet allocation, Cognitive neuroscience of visual object recognition and Pooling.
In his work, Bundle adjustment is strongly intertwined with Margin, which is a subfield of Object.
He combines subjects such as Object and Supervised learning with his study of Natural language processing.
In his study, Backpropagation is inextricably linked to Ranking, which falls within the broad field of Image retrieval.
He most often published in these fields:
- Artificial intelligence (86.34%)
- Computer vision (42.62%)
- Pattern recognition (25.14%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (86.34%)
- Computer vision (42.62%)
- Code (7.65%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, Code, Pose and Object.
His work on Natural language processing expands to the thematically related Artificial intelligence.
His Pose research is multidisciplinary, incorporating elements of Feature and Image retrieval.
His Object research includes elements of Matching, Margin, Bilinear interpolation and Bundle adjustment.
The concepts of his Visualization study are interwoven with issues in Feature extraction, Visual localization, View synthesis and Scale.
His Robot research incorporates themes from Synthetic data, Benchmark, Human–computer interaction and Reinforcement learning.
Between 2019 and 2021, his most popular works were:
- End-to-End Learning of Visual Representations From Uncurated Instructional Videos (98 citations)
- CosyPose: Consistent multi-view multi-object 6D pose estimation (19 citations)
- CosyPose: Consistent multi-view multi-object 6D pose estimation (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pose, Visualization and Code.
His is doing research in Ground truth, Benchmark, Feature, Augmented reality and Visual localization, both of which are found in Artificial intelligence.
His Computer vision research incorporates elements of Tree, Monte Carlo tree search, Robot and Robotic arm.
His studies deal with areas such as Image retrieval and Scale as well as Pose.
His Visualization research is multidisciplinary, incorporating perspectives in End-to-end principle, Segmentation, Task analysis and Natural language processing.
His Code investigation overlaps with Bundle adjustment, Margin, Object, Matching and Feature extraction.
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