Leveraging the powerful capabilities of large language models (LLMs), large vision-language models (LVLMs) can perform a wide variety of tasks based on input images and user instructions. However, existing pathology-focused LVLMs are limited to relatively simple tasks, such as image captioning, visual question answering, and generating brief pathology reports, which restricts their clinical applicability. To enhance the practicality of pathology LVLMs and explore their performance boundaries across pathology tasks, we curated a multi-task pathology instruction-following dataset that better aligns with clinical needs. This dataset encompasses tasks such as cancer classification and grading, molecular subtype identification, and the detection of structures like nuclei, blood vessels, nerves, and lymph nodes. Extensive experiments were conducted on this dataset to identify key factors influencing the performance of LVLMs on these pathology tasks, and optimal solutions were proposed. Our findings provide valuable insights to advance the clinical application of large vision-language models in pathology.

With the rise of AIGC technologies, particularly diffusion models, highly realistic fake images that can deceive human visual perception has become feasible. Consequently, various forgery detection methods have emerged. However, existing methods treat the generation process of fake images as either a black-box or an auxiliary tool, offering limited insights into its underlying mechanisms. In this paper, we propose Spatio-Temporal Distribution Fitting Deviation (STD-FD) for AIGC forgery detection, which explores the generative process in detail. By decomposing and reconstructing data within generative diffusion models, initial experiments reveal temporal distribution fitting deviations during the image reconstruction process. These deviations are captured through reconstruction noise maps for each spatial semantic unit, derived via a super-resolution algorithm. Critical discriminative patterns, termed DFactors, are identified through statistical modeling of these deviations. Extensive experiments show that STD-FD effectively captures distribution patterns in AIGC-generated data, demonstrating strong robustness and generalizability while outperforming state-of-the-art (SOTA) methods on major datasets. The source code is available at [this link](https://github.com/HengruiLou/STDFD).

With the swift progression of image generation technology, the widespread emergence of facial deepfakes poses significant challenges to the field of security, thus amplifying the urgent need for effective deepfake detection. Existing techniques for face forgery detection can broadly be categorized into two primary groups: visual-based methods and multimodal approaches. The former often lacks clear explanations for forgery details, while the latter, which merges visual and linguistic modalities, is more prone to the issue of hallucinations.To address these shortcomings, we introduce a visual detail enhanced self-correction framework, designated CorrDetail, for interpretable face forgery detection. CorrDetail is meticulously designed to rectify authentic forgery details when provided with error-guided questioning, with the aim of fostering the ability to uncover forgery details rather than yielding hallucinated responses. Additionally, to bolster the reliability of its findings, a visual fine-grained detail enhancement module is incorporated, supplying CorrDetail with more precise visual forgery details. Ultimately, a fusion decision strategy is devised to further augment the model's discriminative capacity in handling extreme samples, through the integration of visual information compensation and model bias reduction. Experimental results demonstrate that CorrDetail not only achieves state-of-the-art performance compared to the latest methodologies but also excels in accurately identifying forged details, all while exhibiting robust generalization capabilities.

In recent times, audio-driven lip-synching generation for digital humans has attracted considerable attention. However, the prevailing methodologies frequently encounter challenges pertaining to elevated computational complexity and deficient real-time performance. Although the MuseTalk framework has achieved notable progress in inference efficiency through its end-to-end, latent-space-based single-step generation algorithm, it still suffers from noticeable lip jitter and insufficient synchronization between audio and lip movements. To address these limitations, we propose an enhanced multi-frame inpainting framework that integrates Variational Autoencoders (VAE) and a multi-scale U-Net architecture. Specifically, our approach directly synthesizes the occluded lip region by leveraging multi-frame visual references combined with corresponding audio embeddings, thereby effectively improving lip synchronization and maintaining identity consistency. Furthermore, we introduce a landmark-guided multi-frame sampling strategy designed to enhance model attention towards lip dynamics. To facilitate deeper feature extraction and fusion, we propose a hierarchical latent-space feature fusion network (FusionNet), incorporating global and local residual connections and an enhanced Convolutional Block Attention Module. Additionally, a frame interpolation technique is employed during inference to further smooth lip movements and significantly mitigate lip jitter. The model has been trained on a large-scale Chinese dataset and comprehensively evaluated using both Chinese and English datasets. The experimental results demonstrate that the proposed framework achieves high visual accuracy, consistent lip synchronization, and efficient real-time inference, highlighting its strong cross-lingual generalization capability.