  The depth/thermal information is beneficial for detecting salient object with
conventional RGB images. However, in dual-modal salient object detection (SOD)
model, the robustness against noisy inputs and modality missing is crucial but
rarely studied. To tackle this problem, we introduce \textbf{Co}nditional
Dropout and \textbf{LA}nguage-driven(\textbf{CoLA}) framework comprising two
core components. 1) Language-driven Quality Assessment (LQA): Leveraging a
pretrained vision-language model with a prompt learner, the LQA recalibrates
image contributions without requiring additional quality annotations. This
approach effectively mitigates the impact of noisy inputs. 2) Conditional
Dropout (CD): A learning method to strengthen the model's adaptability in
scenarios with missing modalities, while preserving its performance under
complete modalities. The CD serves as a plug-in training scheme that treats
modality-missing as conditions, strengthening the overall robustness of various
dual-modal SOD models. Extensive experiments demonstrate that the proposed
method outperforms state-of-the-art dual-modal SOD models, under both
modality-complete and modality-missing conditions. We will release source code
upon acceptance.
