Amran Bhuyian

Noakhali Science and Technology University

Papers

Total Citations

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About

Amran Bhuyian is a researcher whose work sits at the intersection of computer vision and deep learning, with a particular focus on person re-identification and multi-modal data fusion. His most-cited paper, "Cross-modal distillation for RGB-depth person re-identification" (2022), has garnered 32 citations, demonstrating its impact in addressing the challenge of matching individuals across different visual modalities. Bhuyian’s major contribution lies in developing a cross-modal distillation framework that effectively transfers knowledge between RGB and depth domains, enabling more robust person re-identification in complex, real-world environments where lighting or occlusions hinder single-modality approaches. This work is notable for its practical implications in surveillance, robotics, and human-computer interaction, where accurate identification across heterogeneous sensors is critical. By bridging the gap between RGB and depth data, Bhuyian has advanced the field’s ability to handle multi-modal inputs, offering a scalable solution for systems that rely on diverse visual cues. His research underscores a commitment to enhancing model generalization and efficiency, making him a promising voice in the evolving landscape of multi-modal learning and its applications in intelligent vision systems.