Medical image segmentation remains challenging in practical deployment, as models often struggle to generalize beyond the distributions covered by their training data and high-quality pixel-level annotations are typically unavailable for adaptation. Inspired by the cross-task transferability of large language models, w...
Xiao-Ye Liang, Ye Yan, Ming-Ze Yin et al.· 0 citations
For RGB-D salient object detection (SOD), a fundamental challenge lies in establishing effective cross-modality interactions between the input graphic domain (RGB and depth modalities) and the output saliency domain. While existing deep learning methods primarily focus on modeling image-level consistency through carefu...
Jingyi Xu, Xin Deng, Minglang Qiao et al.· IEEE Transactions on Pattern...· 0 citations
This study empirically confirms the presence of LLM values, accurately quantifies their shifts, and achieves more efficient and precise steering than conventional blind training, all without degrading general capabilities.
Kelvin Zhang, Jing-Yu-Gin Chen, Yu-Fan Liu et al.· 0 citations
For multi-modal image super-resolution (MISR), exploring cross-modal consistency is of vital importance. However, most existing consistency priors struggle to preserve high-frequency components and fail to provide generalizable regularization, often resulting in blurred edges or inaccurate textures. In this paper, we r...
Jingyi Xu, Xin Deng, Yutong Wang et al.· IEEE Transactions on Pattern...· 0 citations
Recent advancements in deep learning have significantly propelled the enhancement of video compression frameworks, encompassing both encoder-side and postprocessing methods. However, these extensively explored methodologies have reached their limits, offering diminishing returns for further improvement. To overcome the...
Mai Xu, Yichen Guo, Shang-Mou Zhang et al.· IEEE Transactions on Pattern...· 0 citations
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