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Hanqiang Liu

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4 papers

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AAAI Conference 2026 Conference Paper

Frequency-Aware Vision-Language Multimodality Generalization Network for Remote Sensing Image Classification

Junjie Zhang
Feng Zhao
Hanqiang Liu
Jun Yu

The booming remote sensing (RS) technology is giving rise to a novel multimodality generalization task, which requires the model to overcome data heterogeneity while possessing powerful cross-scene generalization ability. Moreover, most vision-language models usually describe surface materials using universal texts, lacking proprietary linguistic prior knowledge specific to different RS modalities. In this work, we formalize RS multimodality generalization (RSMG) as a learning paradigm, and propose a frequency-aware vision-language multimodality generalization network (FVMGN) for RS image classification. Specifically, a diffusion-based training-test-time augmentation (DTAug) strategy is designed to reconstruct multimodal land-cover distributions, enriching input information for FVMGN. Following that, to overcome multimodal heterogeneity, a multimodal wavelet disentanglement (MWDis) module is developed to learn cross-domain invariant features by resampling low and high frequency components in the frequency domain. Considering the characteristics of RS vision modalities, shared and proprietary class texts is designed as linguistic inputs for the transformer-based text encoder to extract diverse text features. For multimodal vision inputs, a spatial-frequency-aware image encoder (SFIE) is constructed to realize local-global feature reconstruction and representation. Finally, a multiscale spatial-frequency feature alignment (MSFFA) module is suggested to construct a unified semantic space, ensuring refined multiscale alignment of different text and vision features in spatial and frequency domains. Extensive experiments show that FVMGN has the excellent multimodality generalization ability compared with state-of-the-art methods.

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EAAI Journal 2024 Journal Article

Data and knowledge-driven dual surrogate-assisted multi-objective rough fuzzy clustering algorithm for image segmentation

Feng Zhao
Caini Lu
Hanqiang Liu

Most multi-objective clustering algorithms (MOCAs) do not fully utilize the spatial and edge information of an image in image segmentation areas. Moreover, the objective evaluations are generally expensive for MOCAs, because the computation cost is related to the number of image pixels. Introducing approximate predictions of surrogate model to replace extensive objective evaluations can improve segmentation efficiency of MOCAs. However, accurately fitting objective functions using only a single surrogate is challenging. To resolve the above-mentioned issues, a data and knowledge-driven dual surrogate-assisted multi-objective rough fuzzy clustering algorithm (DK-DSMRFC) is proposed. First, an edge information-guided local neighborhood weighted filtering strategy is designed to obtain the spatial information with rich image details. Second, three complementary clustering objective functions are constructed to recognize complex clustering structures, which focus on rough fuzzy intra-class compactness with multi-level image information, dual centroids-based inter-class separation, and neighborhood consistency, respectively. To efficiently optimize these objective functions, we construct a data and knowledge-driven dual-surrogate assisted evolutionary framework, in which the radial basis function is used as a principal surrogate model to predict objective functions, and the Kriging model is adopted as an assistant surrogate to provide uncertainty information of predictions. Furthermore, a knowledge-induced multi-perspective infill sampling criterion is designed to promote exploration and exploitation. Finally, a rough fuzzy clustering validity index with spatial constraints and neighborhood consistency is constructed to select the optimal individual. The performance of evolutionary framework is verified on benchmark functions. Experiments on images from four datasets confirm the effectiveness and robustness of the DK-DSMRFC. Keywords: Image segmentation, Rough fuzzy clustering, Surrogate assisted multi-objective optimization, Data and knowledge-driven optimization.

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EAAI Journal 2024 Journal Article

Ensemble CART surrogate-assisted automatic multi-objective rough fuzzy clustering algorithm for unsupervised image segmentation

Feng Zhao
Zihan Tang
Zhilei Xiao
Hanqiang Liu
Jiulun Fan
Lu Li

Multi-objective clustering algorithms (MOCAs) are popular in unsupervised image segmentation due to their merit of meeting multiple segmentation requirements and the prospect of automatically estimating the number of clusters. However, most of them suffer from high time costs and are easily to be influenced by the uncertainty when handling real complex images. To address these issues, we propose an ensemble classification and regression tree (CART) surrogate-assisted automatic multi-objective rough fuzzy clustering (ECS-AMRFC) algorithm for unsupervised image segmentation. Firstly, a cluster medoid-based encoding scheme is employed to represent solutions with different number of clusters and meanwhile lessen the length of encoding. Then, we design an ensemble CART as the surrogate model to significantly reduce the computational burden. Moreover, a surrogate model management strategy is proposed to accelerate the optimization and enhance the quality of surrogate modeling. To handle the uncertainty in data, we extend the rough fuzzy clustering into MOCAs and construct three complementary objective functions to seek proper cluster medoids from multiple perspectives. In addition, the Gaussian kernel is introduced into the objective functions to handle image pixels that cannot separate linearly in the feature space. Finally, a kernelized rough fuzzy clustering validity index is defined to automatically select the optimal solution with no requirements of any prior knowledge. Experiments show that ECS-AMRFC not only identifies appropriate number of clusters on different kinds of images, but also obtains better segmentation results than state-of-the-art rough fuzzy clustering algorithms and automatic MOCAs.

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EAAI Journal 2024 Journal Article

Lightweight anchor-free one-level feature indoor personnel detection method based on transformer

Feng Zhao
Yongheng Li
Hanqiang Liu
Junjie Zhang
Zhenglin Zhu

Owing to the development of deep learning, indoor personnel detection methods based on deep neural networks have been extensively investigated in recent years. However, more complex and deeper network structures may consume more computational resources, which seriously limits the deployment of large-scale deep neural networks on lightweight devices. In view of this, a lightweight anchor-free one-level feature indoor personnel detection method based on transformer (LAOF-IPDT) is proposed in this paper, which is deployed on two embedded devices and achieves good detection accuracy. In the feature extraction backbone, an enhanced cross-stage partial ghost convolution block with ghost convolution and channel shuffle is designed to extract shallow features. Additionally, to obtain more comprehensive global features in the high-level semantic structure, an embedded vision transformer cross-stage partial block is constructed by embedding a lightweight mobile-friendly vision transformer. For the path-aggregation neck, a lightweight feature pyramid network is proposed, which integrates multi-scale feature maps to obtain richer one-level feature representations. Subsequently, a dilated convolution group block is applied to expand the one-level feature receptive field and detection is accomplished using a one-level feature map. For the detection head, an anchor-free mechanism is applied to reduce the hyper-parameter interference of anchor boxes. Extensive experiments on four datasets indicated that the LAOF-IPDT outperforms other lightweight networks in terms of accuracy, speed, model parameters, and model size. For example, the frames per second of the LAOF-IPDT are 8. 39 and 14. 67 on CPU devices and Jetson Nano devices, respectively, and the mean average precision is 84. 49%.

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