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Honghui Xu

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5

AAAI Conference 2026 Conference Paper

TRT: Harnessing Tensor Ring Transformer for Hyperspectral Image Super-Resolution

  • Honghui Xu
  • Junwei Zhu
  • Yubin Gu
  • Yueqian Quan
  • Chuangjie Fang
  • Hong Qiu
  • Jianwei Zheng

Deep unfolding networks (DUNs) have recently emerged as a promising approach for hyperspectral image super-resolution (HSISR) by combining the benefits of nonlinear deep learning architectures with interpretable optimization techniques. Despite their advantages, current DUNs face significant challenges, particularly in approximating degradation matrices across both spatial and spectral dimensions, which results in complex and cumbersome model construction. By analyzing the difference between the upsampled low-resolution hyperspectral images (LRHS) and the true target image, we observed that the residual image exhibits strong sparsity, akin to noise. Leveraging this insight, we reformulate the HSISR problem as a robust principal component analysis (RPCA)-based denoising task, effectively eliminating the need for the complex approximation of spatial degradation matrix and its transpose. In addition, we introduce a Tensor Ring Transformer based on multilinear products as the prior term, wherein tokens are mapped to a tensor ring factor domain and the traditional dot product is replaced with a multilinear tensor ring product. This significantly reduces the computational complexity of the Transformer model, from \( \mathcal{O}(N^2d) \) to \( \mathcal{O}(Nr^2) \), with \( r<

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YNIMG Journal 2025 Journal Article

The modulation of selective attention and divided attention on cross-modal congruence

  • Honghui Xu
  • Guochun Yang
  • Florian Göschl
  • Qiaoyue Ren
  • Mei Yu
  • Qi Li
  • Xun Liu

Previous studies have demonstrated that performance under selective attention and divided attention can be enhanced or impaired, depending on whether the stimuli from different modalities are the same or different. However, it remains unclear whether the modulation of selective attention and divided attention on cross-modal congruence involves shared or distinct neural mechanisms. To clarify this, the present study adopted an audiovisual Stroop task (measuring selective attention) and an audiovisual Matching task (measuring divided attention), using the same physical stimuli, along with event-related potential (ERP) and time-frequency measures. The behavioral results revealed better performance when the auditory and visual stimuli were the same in both tasks. Electroencephalography (EEG) results revealed that different auditory and visual stimuli elicited increased N2 and late positive component (LPC) amplitudes, as well as increased theta power, in both tasks. Moreover, in the audiovisual Matching task, the P3 amplitude was lower in the different condition, and the delta power was greater in the same condition. However, in the audiovisual Stroop task, the amplitude of the N450 component was greater, and beta power was lower, in the different condition. These results indicate that both shared and distinct neural mechanisms underlie the modulation of different types of attention on cross-modal congruence.

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AAAI Conference 2024 Conference Paper

SyFormer: Structure-Guided Synergism Transformer for Large-Portion Image Inpainting

  • Jie Wu
  • Yuchao Feng
  • Honghui Xu
  • Chuanmeng Zhu
  • Jianwei Zheng

Image inpainting is in full bloom accompanied by the progress of convolutional neural networks (CNNs) and transformers, revolutionizing the practical management of abnormity disposal, image editing, etc. However, due to the ever-mounting image resolutions and missing areas, the challenges of distorted long-range dependencies from cluttered background distributions and reduced reference information in image domain inevitably rise, which further cause severe performance degradation. To address the challenges, we propose a novel large-portion image inpainting approach, namely the Structure-Guided Synergism Transformer (SyFormer), to rectify the discrepancies in feature representation and enrich the structural cues from limited reference. Specifically, we devise a dual-routing filtering module that employs a progressive filtering strategy to eliminate invalid noise interference and establish global-level texture correlations. Simultaneously, the structurally compact perception module maps an affinity matrix within the introduced structural priors from a structure-aware generator, assisting in matching and filling the corresponding patches of large-proportionally damaged images. Moreover, we carefully assemble the aforementioned modules to achieve feature complementarity. Finally, a feature decoding alignment scheme is introduced in the decoding process, which meticulously achieves texture amalgamation across hierarchical features. Extensive experiments are conducted on two publicly available datasets, i.e., CelebA-HQ and Places2, to qualitatively and quantitatively demonstrate the superiority of our model over state-of-the-arts.

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