Image-based Virtual Try-On (VTON) aims to generate virtual try-on results by transferring input garment images onto target person images. Current algorithms have achieved high generalization and quality on public datasets including VITON and DressCode. However, current algorithms primarily process images at the pixel level while neglecting the semantic information of garments. Moreover, layered outerwear, as an essential aspect of try-on, has been largely overlooked. Layering increases the difficulty of precise garment control due to the need to consider inner garments. Furthermore, the task-specific nature of current methods prevents them from capturing layering characteristics in try-on. To address these limitations, we propose LayerDiffusion, a novel framework that enhances both generalization capability and semantic controllability for virtual try-on generation. Our approach makes three key contributions. First, we construct LayerDataset, a specialized dataset focusing on diverse outerwear garments with balanced gender representation and multi-view captures. Second, we integrate a pretrained image encoder to capture rich semantic garment information, combined with multi-conditional masking and adapter fine-tuning to enable flexible layering control. Third, we introduce a joint training strategy on both try-on and try-off tasks, which substantially improves model generalization by learning bidirectional garment-body correlations. Extensive experiments demonstrate that LayerDiffusion significantly outperforms existing state-of-the-art methods on public benchmarks (VITON-HD and DressCode) across multiple metrics including LPIPS, SSIM, FID, KID, and CLIP-based scores. Our method excels in both single-garment and layered try-on scenarios while naturally supporting try-off capabilities, offering superior semantic preservation and detail fidelity compared to existing approaches.

Few-shot defect multi-classification (FSDMC) is an emerging trend in quality control within industrial manufacturing. However, current FSDMC research often lacks generalizability due to its focus on specific datasets. Additionally, defect classification heavily relies on contextual information within images, and existing methods fall short of effectively extracting this information. To address these challenges, we propose a general FSDMC framework called MVREC, which offers two primary advantages: (1) MVREC extracts general features for defect instances by incorporating the pre-trained AlphaCLIP model. (2) It utilizes a region-context framework to enhance defect features by leveraging mask region input and multi-view context augmentation. Furthermore, Few-shot Zip-Adapter(-F) classifiers within the model are introduced to cache the visual features of the support set and perform few-shot classification. We also introduce MVTec-FS, a new FSDMC benchmark based on MVTec AD, which includes 1228 defect images with instance-level mask annotations and 46 defect types. Extensive experiments conducted on MVTec-FS and four additional datasets demonstrate its effectiveness in general defect classification and its ability to incorporate contextual information to improve classification performance.

Image-to-image (i2i) translation has achieved notable success, yet remains challenging in scenarios like real-to-illustrative style transfer of fashion. Existing methods focus on enhancing the generative model with diversity while lacking ID-preserved domain translation. This paper introduces a novel model named Uni-DlLoRA to release this constraint. The proposed model combines the original images within a pretrained diffusion-based model using the proposed Uni-adapter extractors, while adopting the proposed Dual-LoRA module to provide distinct style guidance. This approach optimizes generative capabilities and reduces the number of additional parameters required. In addition, a new multimodal dataset featuring higher-quality images with captions built upon an existing real-to-illustration dataset is proposed. Experimentation validates the effectiveness of our proposed method.