Automated identification of bacterial flagellar motors in cryo-electron tomography (cryo-ET) remains a significant challenge due to low signal-to-noise ratios, imaging artifacts, and the complexity of capturing both local and global structural contexts. In this work, we propose MSVT+AUM, a hybrid framework that combines a Multi-Scale Volumetric Transformer (MSVT) with Adaptive Uncertainty Modeling (AUM) to address these limitations. MSVT employs a 3D ResNet-50 backbone to extract hierarchical volumetric features, which are integrated through a volume-level self-attention mechanism and refined via 3D Vision Transformer blocks with positional encoding. To enhance discriminative capability in densely packed molecular environments, we introduce a Contextual Spatial Attention module that adaptively reweights spatial and channel-wise representations. To reduce false positives and provide calibrated confidence estimates, AUM jointly models aleatoric and epistemic uncertainties using heteroscedastic regression and Monte Carlo dropout, with detection thresholds dynamically adjusted based on uncertainty estimates. Evaluation on the BYU flagellar motor dataset and an extended subset of the CryoET Data Portal shows that our method achieves an F 2 score of 0.891, representing a 1.5 % improvement over existing approaches, while reducing in-ference time by 23%. MSVT+AUM exhibits robust generalization across diverse bacterial species and noise conditions, offering a scalable and reliable solution for high-throughput structural analysis in cryo-ET.

Open-vocabulary mobile manipulation (OVMM) that involves the handling of novel and unseen objects across different workspaces remains a significant challenge for real-world robotic applications. In this paper, we propose a novel Language-conditioned Open-Vocabulary Mobile Manipulation framework, named LOVMM, incorporating the large language model (LLM) and vision-language model (VLM) to tackle various mobile manipulation tasks in household environments. Our approach is capable of solving various OVMM tasks with free-form natural language instructions (e.g. "toss the food boxes on the office room desk to the trash bin in the corner", and "pack the bottles from the bed to the box in the guestroom"). Extensive experiments simulated in complex household environments show strong zero-shot generalization and multi-task learning abilities of LOVMM. Moreover, our approach can also generalize to multiple tabletop manipulation tasks and achieve better success rates compared to other state-of-the-art methods.

Video Anomaly Detection (VAD) identifies unexpected events by learning normal behavior from surveillance footage, assuming only normal training data is available. Previous methods, which focus on frame reconstruction or prediction tasks, are constrained by insufficient semantic sensitivity due to a reliance on pixel-level errors and inadequate adaptability to diverse normal patterns. Although contrastive learning methods attempt to address these issues by learning normal subcategories through manually constructed positive-negative pairs, they still encounter semantic ambiguity arising from inappropriate contrastive strategies. To overcome these limitations, we propose a Contrastive Cross-modal Prototype Prediction and Fusion (C2P2F) framework. Specifically, our method comprises three stages: (1) First, the Contrastive Prototype Prediction (CPP) module separately learns normal patterns of appearance and motion on RGB frames and optical flow inputs. Without constructing positive-negative pairs, we perform a cross-view prototype prediction task to discern inherent normal patterns within the data. (2) Then, the Cross-Modal Prototype Fusion (CMPF) conducts alternative training with RGB and optical flow inputs to establish comprehensive normal representations from two complementary modalities, enforcing consistency between cross-modal prototypes and learning semantically rich normal patterns. (3) Finally, the Prototype Number Adjustment (PNA) module is employed to mitigate initialization bias. Integrating these components, our approach adaptively models diverse normalcy and enhances anomaly discrimination via joint optimization of prototype stability and cross-modal consistency, with experiments on three benchmarks showing its superiority.