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Junchi Bin

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2 papers

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AAAI Conference 2023 Short Paper

Lightweight Transformer for Multi-Modal Object Detection (Student Abstract)

Yue Cao
Yanshuo Fan
Junchi Bin
Zheng Liu

It has become a common practice for many perceptual systems to integrate information from multiple sensors to improve the accuracy of object detection. For example, autonomous vehicles use visible light, and infrared (IR) information to ensure that the car can cope with complex weather conditions. However, the accuracy of the algorithm is usually a trade-off between the computational complexity and memory consumption. In this study, we evaluate the performance and complexity of different fusion operators in multi-modal object detection tasks. On top of that, a Poolformer-based fusion operator (PoolFuser) is proposed to enhance the accuracy of detecting targets without compromising the efficiency of the detection framework.

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AAAI Conference 2022 Short Paper

A Multimodal Fusion-Based LNG Detection for Monitoring Energy Facilities (Student Abstract)

Junchi Bin
Choudhury A. Rahman
Shane Rogers
Shan Du
Zheng Liu

Fossil energy products such as liquefied natural gas (LNG) are among Canada’s most important exports. Canadian engineers devote themselves to constructing visual surveillance systems for detecting potential LNG emissions in energy facilities. Beyond the previous infrared (IR) surveillance system, in this paper, a multimodal fusion-based LNG detection (MFLNGD) framework is proposed to enhance the detection quality by the integration of IR and visible (VI) cameras. Besides, a Fourier transformer is developed to fuse IR and VI features better. The experimental results suggest the effectiveness of the proposed framework.

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