Panlong Tan
Papers
2
Total Citations
19
H-Index
2
About
Panlong Tan is a rising researcher in the field of computer vision, with a focused expertise in salient object detection (SOD) under challenging multimodal conditions. His work centers on developing advanced deep learning architectures that fuse complementary visual data—specifically RGB, depth, and thermal infrared imagery—to improve the accuracy and robustness of object segmentation in complex environments. Tan’s major contributions include the introduction of an adaptively cooperative dynamic fusion network for RGB-D SOD, and a transformer-based adaptive interactive promotion network for RGB-Thermal SOD. These innovations address critical challenges in robot decision-making and autonomous systems, where thermal infrared data can significantly enhance performance in low-light or occluded scenes. Although early in his career, his papers have already garnered attention, with his most cited work accumulating 13 citations. His research is particularly notable for its practical implications in robotics and intelligent surveillance, demonstrating a clear pathway from algorithmic design to real-world application. Tan’s work represents a promising trajectory in multimodal perception, positioning him as an emerging voice in the next generation of vision researchers.
Research Focus
Key Achievements
Top Papers
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