Sergey Karayev
Papers
2
Total Citations
27
H-Index
2
About
Sergey Karayev’s research centers on computer vision and machine learning, with a particular focus on 3-D object detection and visual recognition. His most-cited work, “Practical 3-D Object Detection Using Category and Instance-Level Appearance Models” (2011), has garnered 18 citations, showcasing his early contributions to bridging category-level and instance-level appearance models for robust object detection in real-world scenes. This work addresses the challenge of detecting objects under varying viewpoints and occlusions, a key problem in autonomous systems and robotics. Karayev’s approach integrates geometric and appearance cues, enabling practical deployment in environments where precise models are unavailable. Beyond this, his research has influenced fields like scene understanding and visual search, with additional citations reflecting his impact on efficient detection pipelines. Karayev’s contributions are notable for their emphasis on practical, scalable solutions—moving beyond theoretical frameworks to real-world applicability. His work has been cited in contexts ranging from robotics navigation to augmented reality, underscoring its interdisciplinary relevance. For students and researchers, Karayev’s research exemplifies how combining geometric reasoning with appearance models can advance object detection, offering a foundation for further exploration in 3-D vision and embodied AI.
Research Focus
Key Achievements
Top Papers
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- 2