Text-conditioned State Space Model For Domain-generalized Change Detection Visual Question Answering

• URL: http://arxiv.org/abs/2508.08974v3
• Date: Fri, 24 Oct 2025 10:53:51 GMT
• Title: Text-conditioned State Space Model For Domain-generalized Change Detection Visual Question Answering
• Authors: Elman Ghazaei, Erchan Aptoula,
• Abstract summary: Change detection methods typically require expert knowledge for accurate interpretation.<n>New multi-modal and multi-domain dataset, BrightVQA, is introduced to facilitate domain generalization research.<n>Text-Conditioned State Space Model (TCSSM) framework is proposed to leverage both bi-temporal imagery and geo-disaster-related textual information.
• Score: 4.698129958118586
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The Earth's surface is constantly changing, and detecting these changes provides valuable insights that benefit various aspects of human society. While traditional change detection methods have been employed to detect changes from bi-temporal images, these approaches typically require expert knowledge for accurate interpretation. To enable broader and more flexible access to change information by non-expert users, the task of Change Detection Visual Question Answering (CDVQA) has been introduced. However, existing CDVQA methods have been developed under the assumption that training and testing datasets share similar distributions. This assumption does not hold in real-world applications, where domain shifts often occur. In this paper, the CDVQA task is revisited with a focus on addressing domain shift. To this end, a new multi-modal and multi-domain dataset, BrightVQA, is introduced to facilitate domain generalization research in CDVQA. Furthermore, a novel state space model, termed Text-Conditioned State Space Model (TCSSM), is proposed. The TCSSM framework is designed to leverage both bi-temporal imagery and geo-disaster-related textual information in an unified manner to extract domain-invariant features across domains. Input-dependent parameters existing in TCSSM are dynamically predicted by using both bi-temporal images and geo-disaster-related description, thereby facilitating the alignment between bi-temporal visual data and the associated textual descriptions. Extensive experiments are conducted to evaluate the proposed method against state-of-the-art models, and superior performance is consistently demonstrated. The code and dataset will be made publicly available upon acceptance at https://github.com/Elman295/TCSSM.

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