Ming Meng

Ming Meng, Qi Dong, Jiajie Li et al.

Virtual try-on technology has become increasingly important in the fashion and retail industries, enabling the generation of high-fidelity garment images that adapt seamlessly to target human models. While existing methods have achieved notable progress, they still face significant challenges in maintaining consistency across different poses. Specifically, geometric distortions lead to a lack of spatial consistency, mismatches in garment structure and texture across poses result in semantic inconsistency, and the loss or distortion of fine-grained details diminishes visual fidelity. To address these challenges, we propose HF-VTON, a novel framework that ensures high-fidelity virtual try-on performance across diverse poses. HF-VTON consists of three key modules: (1) the Appearance-Preserving Warp Alignment Module (APWAM), which aligns garments to human poses, addressing geometric deformations and ensuring spatial consistency; (2) the Semantic Representation and Comprehension Module (SRCM), which captures fine-grained garment attributes and multi-pose data to enhance semantic representation, maintaining structural, textural, and pattern consistency; and (3) the Multimodal Prior-Guided Appearance Generation Module (MPAGM), which integrates multimodal features and prior knowledge from pre-trained models to optimize appearance generation, ensuring both semantic and geometric consistency. Additionally, to overcome data limitations in existing benchmarks, we introduce the SAMP-VTONS dataset, featuring multi-pose pairs and rich textual annotations for a more comprehensive evaluation. Experimental results demonstrate that HF-VTON outperforms state-of-the-art methods on both VITON-HD and SAMP-VTONS, excelling in visual fidelity, semantic consistency, and detail preservation.

Xinran Xu, Yuliang Ma, Sifu Cai et al.

Retinal vessel segmentation is crucial for intelligent ophthalmic diagnosis, yet it faces three major challenges: insufficient multi-scale feature fusion, disruption of contextual continuity, and noise interference. This study proposes a dual-stage solution to address these issues. The first stage employs a Reversible Multi-Scale Fusion Module (RMS) that uses hierarchical adaptive convolution to dynamically merge cross-scale features from capillaries to main vessels, self-adaptively calibrating feature biases. The second stage introduces a Vascular-Oriented Attention Mechanism, which models long-distance vascular continuity through an axial pathway and enhances the capture of topological key nodes, such as bifurcation points, via a dedicated bifurcation attention pathway. The synergistic operation of these two pathways effectively restores the continuity of vascular structures and improves the segmentation accuracy of complex vascular networks. Systematic experiments on the DRIVE, STARE, and CHASE-DB1 datasets demonstrate that WMKA-Net achieves an accuracy of 0.9909, sensitivity of 0.9198, and specificity of 0.9953, significantly outperforming existing methods. This model provides an efficient, precise, and robust intelligent solution for the early screening of diabetic retinopathy.