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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.
The stealthy transfer attack ST is introduced, a plug-in primal-dual wrapper that adds an $L_\infty$ saturation regularizer to the standard constrained objective and resolves it through a two-step primal-dual update, confirming that the latent imperceptibility advantage of strong transfer attacks can be unlocked by a p...
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...
This work introduces a Variational Semantic Prompt Extractor (VSPE), which adaptively aggregates anomaly-relevant local semantics from dense patch tokens and regularizes them through a variational information bottleneck, thereby incorporating fine-grained visual cues and enabling more precise cross-modal alignment.
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