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Qiaolin Ye

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4 papers

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EAAI Journal 2025 Journal Article

Lightweight binary convolutional-transformers fusion network for facial expression recognition

Xing Jin
Xiyin Wu
Libo Weng
Qiaolin Ye

Deep learning-based methods exploit local facial regions for facial expression recognition (FER) but overlook long-range dependencies of facial muscle movements. Moreover, deploying existing large-scale deep networks on mobile devices remains a huge challenge. To this end, this paper designs a mini-binary transformer (MiniBTR) for FER. Specifically, we first construct image patches by regions of interest (ROIs) which are associated with facial action units (AUs) and represent these patches by the histogram of oriented gradient (HOG) feature. Then, by integrating the complementary advantages of convolutional layers (Convs) and multi-head self-attention mechanism (MSA), an effective and efficient mini-transformer (MiniTR) architecture with 69K parameters is proposed to follow the human–machine collaborative strategy. Finally, we design the binary model MiniBTR by extending MiniTR with a binary operation. Extensive experimental results on four publicly available datasets demonstrate that the MiniBTR yields comparable results in terms of recognition accuracy, model size and inference speed while offering a more deployable yet high-performing alternative.

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AIIM Journal 2025 Journal Article

RobustEMD: Domain robust matching for cross-domain few-shot medical image segmentation

Yazhou Zhu
Minxian Li
Qiaolin Ye
Shidong Wang
Tong Xin
Haofeng Zhang

Few-shot medical image segmentation (FSMIS) aims to perform the limited annotated data learning in the medical image analysis scope. Despite the progress has been achieved, current FSMIS models are all trained and deployed on the same data domain, as is not consistent with the clinical reality that medical imaging data is always across different data domains (e. g. imaging modalities, institutions and equipment sequences). In this paper, we introduce Cross-domain Few-shot Medical Image Segmentation (CD-FSMIS) and propose a RobustEMD matching mechanism based on Earth Mover’s Distance (EMD) to enhance cross-domain generalization. Our approach includes three key components: (1) a channel-wise feature decomposition strategy that uniformly divides support and query features into local nodes, (2) a texture structure aware weights generation method that restrains domain-relevant nodes through Sobel-based gradient calculation, and (3) a boundary-aware Hausdorff distance measurement for transportation cost calculation. Extensive experiments across three scenarios (cross-modal, cross-sequence and cross-institution) show that our method significantly outperforms existing approaches. And ablation studies further confirm that each component of our RobustEMD mechanism contributes to the enhanced performance. The experimental outcomes highlight strong generalization capabilities of our model in real-world heterogeneous medical imaging environments. Code is available at https: //github. com/YazhouZhu19/RobustEMD.

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EAAI Journal 2024 Journal Article

Global superpixel-merging via set maximum coverage

Xubing Yang
Zhengxiao Zhang
Li Zhang
Xijian Fan
Qiaolin Ye
Liyong Fu

Due to better boundary adherence and low computational cost, the superpixel segmentation algorithm SLIC (simple linear iterative clustering) has been widely applied in vision-based applications. However, limit to unavoidable over-segmentation problem, one has to consider region-merging to reconstruct entire objects from the segmented superpixels (or called regions). The existing region-merging methods are generated from data clustering, and avoidably suffer from error-merging, slow convergence speed, or easily dropping in LOCAL optimal problems, especially for high-resolution RS (remote sensing) images. In this paper, instead of data clustering, we propose a fast GLOBAL method based on Set Maximum Coverage, termed as MaxCov-merging. Theoretically, the existence of the maximum coverage is proved by using Bayes optimal decision principle. To speed up MaxCov-merging, some heuristic strategies are also provided. Finally, extensive verification and comparison are carried on the public and our collected high-resolution images. Compared with the state-of-the-art methods, the comparison shows the superiority of our MaxCov in terms of the performance of globality, ease of use and fast region-merging speed.

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EAAI Journal 2021 Journal Article

Pixel-level automatic annotation for forest fire image

Xubing Yang
Run Chen
Fuquan Zhang
Li Zhang
Xijian Fan
Qiaolin Ye
Liyong Fu

We propose an automatic annotation method for forest fire images in the level of pixel, where supervise information is introduced by interactive convex hulls. Instead of usual rectangle-/regular-shaped regions, we propose a convex hull algorithm for visually selecting polygonal (irregular) fire and no-fire regions. Guided by the goals of forest fire monitoring systems: high fire detection rate (true-positive) and then low false alarm rate (false-positive), we construct a k-nearest neighbor (kNN) based KD-tree to speed annotation. Compared to state-of-the-art, the proposed method not only widens the view of fire detection from conventional two-class to multi-class classification problem to meet complex forest image background, but also relaxes the limit of i. i. d (independent and identical distribution) hypothesis on machine learning methods. Furthermore, it is simple to use, which just relies on pixel information and avoids considering additional auxiliary features from multiple color spaces. Experimental evaluations are carrying on forest fire images, MIVIA dead-directional videos, and more challenging omni-directional videos. The comparison demonstrates that the proposed pixel-level annotation method is able to achieve higher fire detection rate and lower false alarm rate at the same time.

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