17 accepted papers
Natural language provides valuable auxiliary information for enhancing visual object tracking. While existing vision-language tracking methods explicitly leverage linguistic descriptions to aid tracking, they suffer from two critical limitations: the inability to dynamically adapt descriptions to th
Neural operators have emerged as powerful surrogates for solving PDEs, significantly accelerating scientific computation. While transformer-based architectures offer unmatched flexibility for irregular domains, they suffer from a fundamental efficiency gap: standard attention mechanisms assign unifo…
Existing deep spatial–spectral fusion (SSF) methods typically learn the fusion mapping in the coordinate domain using convolutions and attentions, making it hard to scale across varying spatial resolutions and offering limited control over the frequency content of the reconstructions, which may furt…
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 challen
Deep unfolding network (DUN) has shed new light on multi-sequence MRI reconstruction, providing both high interpretability and acceptable performance. However, current approaches still suffer from the plight of information isolation, i.e., learning features of multi-suquences individually and leavin…
Interpretability and consistency have long been crucial factors in MRI reconstruction. While interpretability has been significantly innovated with the emerging deep unfolding networks, current solutions still suffer from inconsistency issues and produce inferior anatomical structures. Especially in…
Deep unfolding network (DUN) based pansharpening has shed new light on high-resolution/spectrum image acquisition, serving as a computational alternative to physical devices. While with both merits of deep feature learning and acceptable interpretability enjoyed, current pansharpening necessitates s…
Pansharpening technique is dedicated to enriching the spatial details of low-resolution multispectral images (LRMS) under the guidance of a panchromatic (PAN) image. With the guarantee of promising results, Transformer-based methods have enjoyed a high reputation in this field. However, to reduce co…
Neural operator is considered a popular data-driven alternative to traditional partial differential equation (PDE) solvers. However, most current solutions, whether fulfilling computations in frequency, Laplacian, and wavelet domains, all deviate far from the intrinsic PDE space. While with meticulo…
Benefiting from the booming deep learning techniques, neural operators (NO) are considered as an ideal alternative to break the traditions of solving Partial Differential Equations (PDE) with expensive cost. Yet with the remarkable progress, current solutions concern little on the holistic function…
Compared to the natural image community, infrared target detection suffers more challenges due to the severely tiny and low-contrast objects, especially in cases with obscuration from clutter and noise. The traditional solutions are susceptible to noise interference, which yields suboptimal performa…
Current subject-driven image generation methods encounter significant challenges in person-centric image generation. The reason is that they learn the semantic scene and person generation by fine-tuning a common pre-trained diffusion which involves an irreconcilable training imbalance. Precisely to…
The compressed sensing MRI aims to recover high-fidelity images from undersampled k-space data, which enables MRI acceleration and meanwhile mitigates problems caused by prolonged acquisition time, such as physiological motion artifacts, patient discomfort, and delayed medical care. In this regard,…
Consistency and interpretability have long been the critical issues in MRI reconstruction. While interpretability has been dramatically improved with the employment of deep unfolding networks (DUNs), current methods still suffer from inconsistencies and generate inferior anatomical structure. Especi…
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 distort…
Building change detection of remote sensing images is in full flourishing accompanied by the prosperity of convolutional neural networks. For spatial-temporal context modeling, existing solutions disregard the inter-image interactions, albeit their positive contribution to the acquisition of differe…
Most research on Low-dose Computed Tomography (LDCT) reconstruction is designed as a black box, lacking controllability and interpretability. In this paper, a Proximal Linear ADMM framework-based Generative Adversarial Network (PLA-GAN) is proposed. Specifically, without loss of interpretability, ch…