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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.
DualOPSD, an asymmetric alternating framework that adapts both policies, reduces truncation across all three scales, and makes later supervision responsive to the learner and does not require another rollout.
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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