Die Xiao | Searcharxiv

arXiv2026

Effective Second-Harmonic Generation Coefficient and C-eigenvalue of Nonlinear Susceptibility Tensors

The effective second-harmonic generation (SHG) coefficient is a crucial data that quantifies the efficiency of transforming fundamental frequency light into its second harmonic. With the help of the symmetry of nonlinear optical susceptibility tensors, we mainly discuss the computability of such a effective SHG coefficient in uniaxial crystals. For one thing, the calculation of effective SHG coefficient is converted into the optimization models with some geometric constraints by means of the peculiarity of fundamental frequency light. Secondly, the number of variables of such maximum models are cutted in half to $2$ to calculate it easier, and a comparison between the effective SHG coefficient and C-eigenvalue of susceptibility tensor is given also. Finally, some examples of typical crystal classes are presented to verify the correctness and broader applicabilities of the theoretical results.

math.OC↗

arXiv2025

SSR: Semantic and Spatial Rectification for CLIP-based Weakly Supervised Segmentation

In recent years, Contrastive Language-Image Pretraining (CLIP) has been widely applied to Weakly Supervised Semantic Segmentation (WSSS) tasks due to its powerful cross-modal semantic understanding capabilities. This paper proposes a novel Semantic and Spatial Rectification (SSR) method to address the limitations of existing CLIP-based weakly supervised semantic segmentation approaches: over-activation in non-target foreground regions and background areas. Specifically, at the semantic level, the Cross-Modal Prototype Alignment (CMPA) establishes a contrastive learning mechanism to enforce feature space alignment across modalities, reducing inter-class overlap while enhancing semantic correlations, to rectify over-activation in non-target foreground regions effectively; at the spatial level, the Superpixel-Guided Correction (SGC) leverages superpixel-based spatial priors to precisely filter out interference from non-target regions during affinity propagation, significantly rectifying background over-activation. Extensive experiments on the PASCAL VOC and MS COCO datasets demonstrate that our method outperforms all single-stage approaches, as well as more complex multi-stage approaches, achieving mIoU scores of 79.5% and 50.6%, respectively.

cs.CV↗