Imran Kabir
Papers
1
Total Citations
7
H-Index
1
About
Imran Kabir is a rising researcher in computer vision, with a focus on semantic segmentation and few-shot learning in challenging visual domains. His most cited work, "Few-Shot Segmentation and Semantic Segmentation for Underwater Imagery" (2023, 7 citations), addresses the critical lack of diverse, annotated underwater datasets. Kabir introduced a novel underwater animal-centric dataset with dense pixel-level annotations, enabling fine-grained segmentation of marine species. This contribution directly tackles the scarcity of labeled data in underwater environments, a key bottleneck for autonomous marine monitoring and ecological studies. By combining few-shot learning with traditional semantic segmentation, his work pushes the boundaries of model generalization in data-poor, visually complex settings. Kabir’s research is particularly impactful for applications in marine biology, environmental conservation, and autonomous underwater vehicles. His dataset and methodology provide a foundation for future work in domain adaptation and low-shot learning, making him a notable emerging voice in the intersection of computer vision and marine science.
Research Focus
Key Achievements
Top Papers
- 1Few-Shot Segmentation and Semantic Segmentation for Underwater Imagery7 citations · 2023