Chengliang Liu

When Semantically Consistent Encoding Meets View-Label Heterogeneity Modeling: A Unified Framework for Incomplete Multi-View Multi-Label Learning.

Incomplete multi-view multi-label learning requires not only robust semantic aggregation from partially observed views, but also label-aware exploitation of view-specific evidence. Existing approaches usually emphasize either shared representation learning or decision-level fusion. The former improves robustness agains...

Representation--Behavior Alignment for Explainable Weakly-Supervised Video Anomaly Detection

Multimodal Large Language Models (MLLMs) provide a natural way to make video anomaly detection more explainable. However, their final decisions do not always fully use the discriminative information contained in their hidden states, an issue we refer to as representation--behavior misalignment. We decompose this gap in...

RoMod: Temporal Routing Modulation via Mixture-of-Experts for Video Anomaly Detection

RoMod is proposed, an efficient VAD framework trained with only \(5\%\) of weakly labeled videos that achieves state-of-the-art performance while running substantially faster than dense backbones of comparable size.

Semantic Invariant Representation Learning and Data-Driven Label Prototype Modeling for Incomplete Multi-view Multi-label Classification

Noise-Induced Cross-Modal Information Interaction and Dual-Prompt Learning for Medical Image Segmentation

Accurate medical image segmentation plays a vital role in clinical diagnostics by facilitating the precise delineation of anatomical structures and pathological regions. However, the performance of existing segmentation methods is often constrained by the scarcity of high-quality annotated datasets, as manual labeling...

VideoVIBE: A Video-Grounded Diagnostic Benchmark for One-Shot Interactive Website Generation

VideoVIBE is introduced, a video-grounded benchmark that transforms human-operated webpage recordings into fine-grained diagnostic tasks and V2Lens is proposed, a training-free, evidence-grounded multi-agent system that challenges and selectively refines initial video-based diagnoses through targeted visual and source-...