Shaotian Li
Publications
Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models
Large-scale vision-language models (VLMs) exhibit remarkable zero-shot capabilities, yet the internal mechanisms driving their anomaly detection (AD) performance remain poorly understood. Current methods predominantly treat VLMs as black-box feature extractors, assuming that anomaly-specific knowledge must be acquired through external adapters or memory banks. In this paper, we challenge this assumption by arguing that anomaly knowledge is intrinsically embedded within pre-trained models but remains latent and under-activated. We hypothesize that this knowledge is concentrated within a sparse subset of anomaly-sensitive neurons. To validate this, we propose latent anomaly knowledge excavation (LAKE), a training-free framework that identifies and elicits these critical neuronal signals using only a minimal set of normal samples. By isolating these sensitive neurons, LAKE constructs a highly compact normality representation that integrates visual structural deviations with cross-modal semantic activations. Extensive experiments on industrial AD benchmarks demonstrate that LAKE achieves state-of-the-art performance while providing intrinsic, neuron-level interpretability. Ultimately, our work advocates for a paradigm shift: redefining anomaly detection as the targeted activation of latent pre-trained knowledge rather than the acquisition of a downstream task.
FashionPose: Unified Text-Driven Fashion Synthesis with Joint Geometric and Photometric Control
Realistic and controllable garment synthesis is essential for fashion e-commerce, yet it demands precise coordination between human pose geometry and environmental photometry. Conventional pose-guided frameworks suffer from two fundamental limitations: they rely heavily on predefined skeletons from off-the-shelf estimators, restricting semantic flexibility; and they predominantly focus on studio-like generation under neutral lighting, failing to reconcile geometric configurations with complex, scene-specific illumination described in natural language. To bridge this gap, we propose FashionPose, a cascaded architecture that reconciles geometric and photometric control within a unified language-driven interface. Unlike conventional frameworks, our framework employs a decoupled yet synergistic strategy: (1) a bidirectional contrastive alignment mechanism that grounds textual semantics into an explicit geometric manifold, enabling template-free pose generation; (2) an identity-anchored synthesis module that translates these geometric priors into high-fidelity imagery while preserving fine-grained appearance; and (3) a prompt-conditioned relighting module that leverages the generated pose as a spatial anchor to achieve environment-aware shading. This hierarchical design effectively transforms high-level instructions into consistent visual representations, ensuring both structural precision and atmospheric harmony. To facilitate this paradigm, we construct PoseCap, a dataset with over 40,000 caption-keypoint pairs. Extensive experiments demonstrate that FashionPose outperforms existing benchmarks in pose accuracy and physical realism, providing a robust solution for personalized, scene-aware virtual fashion displays.