7 papers
HarnessWAM: Bridging Prediction and Deliberation in World Action Models
Zhaopeng Gu, Bingke Zhu, Tianxi Lin +8
World Action Models (WAMs) jointly learn environmental dynamics and robot actions, introducing priors over physical evolution into embodied control. However, finite-horizon predict…
DIM-WAM: World-Action Modeling with Diverse Historical Event Memory
Kai Wang, Zhaopeng Gu, Yixiang Chen +7
World-action models have shown promising robot-manipulation performance by jointly predicting future visual states and actions. However, existing methods mainly rely on short-term…
UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction
Zhaopeng Gu, Bingke Zhu, Zhaowen Li +5
Unified visual anomaly detection seeks to train a single detector that can be deployed across categories, domains, and application scenarios. In the few-shot transfer regime, the k…
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization
Zhaopeng Gu, Bingke Zhu, Guibo Zhu +3
Anomaly detection methods typically require extensive normal samples from the target class for training, limiting their applicability in scenarios that require rapid adaptation, su…
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization
Zhaopeng Gu, Bingke Zhu, Guibo Zhu +4
Zero-shot anomaly detection (ZSAD) methods entail detecting anomalies directly without access to any known normal or abnormal samples within the target item categories. Existing ap…
AnomalyMoE: Towards a Language-free Generalist Model for Unified Visual Anomaly Detection
Zhaopeng Gu, Bingke Zhu, Guibo Zhu +4
Anomaly detection is a critical task across numerous domains and modalities, yet existing methods are often highly specialized, limiting their generalizability. These specialized m…