  The existing change detection(CD) methods can be summarized as the
visual-first change detection (ViFi-CD) paradigm, which first extracts change
features from visual differences and then assigns them specific semantic
information. However, CD is essentially dependent on change regions of interest
(CRoIs), meaning that the CD results are directly determined by the semantics
changes of interest, making its primary image factor semantic of interest
rather than visual. The ViFi-CD paradigm can only assign specific semantics of
interest to specific change features extracted from visual differences, leading
to the inevitable omission of potential CRoIs and the inability to adapt to
different CRoI CD tasks. In other words, changes in other CRoIs cannot be
detected by the ViFi-CD method without retraining the model or significantly
modifying the method. This paper introduces a new CD paradigm, the
semantic-first CD (SeFi-CD) paradigm. The core idea of SeFi-CD is to first
perceive the dynamic semantics of interest and then visually search for change
features related to the semantics. Based on the SeFi-CD paradigm, we designed
Anything You Want Change Detection (AUWCD). Experiments on public datasets
demonstrate that the AUWCD outperforms the current state-of-the-art CD methods,
achieving an average F1 score 5.01\% higher than that of these advanced
supervised baselines on the SECOND dataset, with a maximum increase of 13.17\%.
The proposed SeFi-CD offers a novel CD perspective and approach.
