Xiao Qin
Papers
1
Total Citations
16
H-Index
1
About
Xiao Qin is a researcher whose work lies at the intersection of computer vision and natural language processing, with a particular focus on scene text detection. His most-cited paper, "Arbitrary Shape Natural Scene Text Detection Method Based on Soft Attention Mechanism and Dilated Convolution" (2020, 16 citations), addresses a critical challenge in the field: accurately detecting text in irregular shapes, such as curved or arbitrarily oriented characters. This work introduces a novel approach combining soft attention mechanisms with dilated convolutions, significantly improving detection robustness for complex real-world scenes. Qin’s contributions are vital for applications like autonomous driving and robotics, where understanding text in dynamic environments is essential. By advancing methods beyond traditional horizontal and oriented text detection, his research has laid important groundwork for more flexible and reliable text recognition systems. With growing citation impact, Xiao Qin continues to shape the future of intelligent visual understanding.
Research Focus
Key Achievements
Top Papers
- 1