Text-conditioned State Space Model For Domain-generalized Change
  Detection Visual Question Answering
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.
689cead8b083e610d741e92e
Elman Ghazaei
claimed_verified
2025-08-14T13:38:00.504Z
67ff950b9e4824de182acf83
/avatars/d8bae10afdd7457a08ec9554f79c7429.svg
Elman Ghazaei
user
ElmanGhazaei
689cead8b083e610d741e92f
Erchan Aptoula
Text-conditioned State Space Model For Domain-generalized Change
  Detection Visual Question Answering
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.
A novel state space model, TCSSM, is introduced to address domain shift in Change Detection Visual Question Answering (CDVQA) by leveraging bi-temporal imagery and textual information.
Change Detection Visual Question Answering CDVQA domain shift multi-modal multi-domain BrightVQA Text-Conditioned State Space Model TCSSM domain-invariant features bi-temporal imagery geo-disaster-related textual information