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Similarity-Shift Refinement (SSR), a training-free post-hoc method for improving object-centric masks with a frozen self-supervised Vision Transformer, provides a simple and transferable signal for training-free object-centric mask refinement.
Under shared-history workloads, Cached-Linear substantially reduces computation and memory compared with Global-Softmax, while Balanced GDN improves selected direct-prediction endpoints with efficient context reuse.
A question-relevant saliency map identifies and organizes key visual regions, explicitly modeling the spatial distribution of visual importance, and induces a curriculum-like semantic progression from primary to secondary cues, validating structured and sequential visual cognition.
DeltaFlow is a promising alternative to dense attention for efficient continuous language denoising and noise-adaptive memory control and scheduled Temporal State Consistency to stabilize hidden representations across nearby noise levels are introduced.
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