Haihua Xiao

Papers

Total Citations

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About

Haihua Xiao is a researcher advancing the field of computer vision with a focus on cost-effective and efficient object detection systems. His key research areas include deep learning, object detection, and sustainable AI applications, particularly in challenging real-world environments. Xiao’s major contribution is the development of Active Learning-DETR, a novel framework that integrates active learning with the DETR (Detection Transformer) architecture to address the scarcity of labeled data and the high cost of annotation. This work, published in 2024 and garnering 13 citations, tackles the specific problem of detecting kitchen waste—a domain plagued by diverse target categories, morphological variations, and complex backgrounds. By enabling more efficient use of training data, Xiao’s approach reduces the need for extensive manual labeling while maintaining detection accuracy. His research demonstrates a commitment to practical, scalable solutions that bridge the gap between state-of-the-art AI models and real-world constraints. Xiao’s work is particularly notable for its potential impact on environmental sustainability and waste management, offering a pathway to deploy intelligent systems in resource-limited settings.