ai.hackcv
论文精选 82arXiv

CogVis: Must Open-Vocabulary Change Detection Perceive the Scene Anew for Every Query?· CogVis: 开放词汇变化检测需认知记忆引导

Earth-surface monitoring requires change detection models capable of recognizing arbitrary semantic categories. Open-Vocabulary Change Detection (OVCD) addresses this need. However, existing methods often entangle temporal perception, semantic discrimination, and region verification, causing unstable results and redundant computation. Inspired by human visual change perception, we propose CogVis, a cognitive memory-guided framework that reformulates OVCD as a perception-memory-verification paradigm. CogVis first employs a Scene Change Perceptron (SCP) to extract a reusable, category-agnostic change prior from frozen bi-temporal features, thereby decoupling temporal evidence from semantic category decisions. A Semantic Memory Calibrator (SMC) then compensates for category-dependent score sh

领域:cs.AI作者:Zijie Wang、Chen Zhong、Wei He
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