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Domain Adaptive Lung Nodule Detection in X-ray Image

Autor: Zhao, Haifeng, Jiang, Lixiang, Ma, Leilei, Sun, Dengdi, Fu, Yanping

Medical images from different healthcare centers exhibit varied data distributions, posing significant challenges for adapting lung nodule detection due to the domain shift between training and application phases. Traditional unsupervised domain adap

Externí odkaz: http://arxiv.org/abs/2407.19397

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Text-Region Matching for Multi-Label Image Recognition with Missing Labels

Autor: Ma, Leilei, Xie, Hongxing, Wang, Lei, Fu, Yanping, Sun, Dengdi, Zhao, Haifeng

Recently, large-scale visual language pre-trained (VLP) models have demonstrated impressive performance across various downstream tasks. Motivated by these advancements, pioneering efforts have emerged in multi-label image recognition with missing la

Externí odkaz: http://arxiv.org/abs/2407.18520

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Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens

Autor: Sun, Dengdi, Pan, Yajie, Lu, Andong, Li, Chenglong, Luo, Bin

Many RGBT tracking researches primarily focus on modal fusion design, while overlooking the effective handling of target appearance changes. While some approaches have introduced historical frames or fuse and replace initial templates to incorporate

Externí odkaz: http://arxiv.org/abs/2401.01674

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Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification

Autor: Ma, Leilei, Sun, Dengdi, Wang, Lei, Zhao, Haifeng, Luo, Bin

Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent works have focused on modeling la

Externí odkaz: http://arxiv.org/abs/2307.09715

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Akademický článek

EEG-based patient-specific seizure prediction based on Spatial–Temporal Hypergraph Attention Transformer

Autor: Dong, Changxu, Sun, Dengdi, Zhang, Zejing, Luo, Bin

Publikováno v: In Biomedical Signal Processing and Control February 2025 100 Part C

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LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking

Autor: Li, Chenglong, Xue, Wanlin, Jia, Yaqing, Qu, Zhichen, Luo, Bin, Tang, Jin, Sun, Dengdi

RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evalua

Externí odkaz: http://arxiv.org/abs/2104.13202

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