Cross-modal Contrastive Learning for Robust Visual Representation in Dynamic Environmental Conditions

This paper proposes a novel cross-modal contrastive learning framework for robust visual representation under dynamic environmental conditions. We address the challenge of maintaining consistent performance across varying environments by introducing a dual-stream architecture that leverages complementary information from visual and contextual modalities. Our framework incorporates three key components: (1) a cross-modal contrastive learning mechanism that establishes correspondences between modalities while preserving their semantic structure, (2) a feature alignment module with cross-modal attention that dynamically aligns features across modalities, and (3) an environmental adaptation strategy with adaptive normalization and memory-augmented learning to enhance robustness against environmental variations. Extensive experiments on three datasets (DynamicVQA, MultiEnv-ImageText, and RobustSceneX) demonstrate that our approach consistently outperforms existing methods, achieving an average improvement of 8.1% in mean Average Precision over state-of-the-art baselines. Ablation studies confirm the contribution of each component, with the full model exhibiting superior performance in cross-condition scenarios. Zero-shot transfer experiments further validate the generalizability of our learned representations to downstream tasks. Our work provides a comprehensive solution for robust visual representation learning in real-world applications where environmental conditions frequently change.