SeFi-CD: A Semantic First Change Detection Paradigm That Can Detect Any Change You Want

TL;DR

This paper proposes a novel semantic-first change detection paradigm that focuses on perceiving relevant semantics before visually searching for changes, enabling detection of any change of interest without retraining.

Contribution

Introduces the SeFi-CD paradigm that shifts from visual-first to semantics-first change detection, allowing flexible and adaptable detection of various change regions of interest.

Findings

AUWCD outperforms state-of-the-art methods by 5.01% in F1 score on the SECOND dataset.

SeFi-CD achieves higher adaptability without retraining for different CRoI detection tasks.

Demonstrates the effectiveness of semantics-first approach through experiments.

Abstract

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.