Human vision excels at deriving polysemous meanings from sparse structures, such as those in the Gollin Test. However, existing models struggle to balance semantic retrieval with structural consistency under extreme sparsity. We propose ImageNetion, a framework that leverages retrieval-driven generation for creative completion. The core algorithm consists of two stages: (1) Static Training, which establishes basic sketch-text semantic associations through contrastive learning; and (2) Reinforcement Learning, where retrieval is modeled as a decision process. In this stage, a diffusion model integrated with ControlNet and LoRA serves as a feedback source. A self-feedback mechanism enables the recognition model to learn generator priors unsupervised, enhancing both retrieval rationality and completion quality. Evaluated on a new 400-class synthetic dataset and zero-shot hand-drawn tests, ImageNetion outperforms traditional sketch inpainting in structural fidelity and Top-K semantic consistency. Furthermore, user studies validate the framework’s superiority in creative diversity and human preference.

Abstract We propose Hi3DFace, a novel framework for simultaneous de‐occlusion and high‐fidelity 3D face reconstruction. To address real‐world occlusions, we construct a diverse facial dataset by simulating common obstructions and present TMANet, a transformer‐based multi‐scale attention network that effectively removes occlusions and restores clean face images. For the 3D face reconstruction stage, we propose a coarse‐medium‐fine self‐supervised scheme. In the coarse reconstruction pipeline, we adopt a face regression network to predict 3DMM coefficients for generating a smooth 3D face. In the medium‐scale reconstruction pipeline, we propose a novel depth displacement network, DDFTNet, to remove noise and restore rich details to the smooth 3D geometry. In the fine‐scale reconstruction pipeline, we design a GCN (graph convolutional network) refiner to enhance the fidelity of 3D textures. Additionally, a light‐aware network (LightNet) is proposed to distil lighting parameters, ensuring illumination consistency between reconstructed 3D faces and input images. Extensive experimental results demonstrate that the proposed Hi3DFace significantly outperforms state‐of‐the‐art reconstruction methods on four public datasets, and five constructed occlusion‐type datasets. Hi3DFace achieves robustness and effectiveness in removing occlusions and reconstructing 3D faces from real‐world occluded facial images.

With the continuous development of heavy-haul railway technology, the demand for the faster transmission and higher bandwidth capacity of wireless communication network is also increasing. Therefore, Long Term Evolution for Railway (LTE-R) begin to replace Global System for Mobile Communications for Railway (GSM-R) to burden the core wireless communication services. In order to improve the operation and maintenance efficiency of LTE-R network, this paper proposes a multi-step LTE-R communication quality prediction method based on Differential Evolution algorithm and Temporal Convolutional Network with Coordinate Attention (TCNCA). Firstly, this method uses differential evolution and permutation importance index to filter the features of multivariate LTE-R communication quality data. Then, the coordinate attention mechanism and TCN network were fused to build the prediction model. Finally, this method still used Differential Evolution to adjust the model parameters based on Mann—Kendall test, so that the model could take into account the prediction accuracy and the trend of data change, thus realize the multi-step prediction for LTE-R communication quality. Experimental results on real data set show that the proposed method can provide decision support for the active maintenance of LTE-R network, and has high application value.