Accepted 2025 April 14 ; Published 2025 April 15

Academic Journal of Natural Science, 2025, 2(2), 3078-5170.

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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.

Cross-modal Contrastive Learning , Environmental Robustness , Feature Alignment , Visual Representation .

APA Style

Jia, X., Hu, C. & Jia, G. (2025). Cross-modal contrastive learning for robust visual representation in dynamic environmental conditions. Academic Journal of Natural Science, 2(2), 23-34. https://doi.org/10.70393/616a6e73.323833

I would like to extend my sincere gratitude to Wenkun Ren, Xingpeng Xiao, Jian Xu, Heyao Chen, Yaomin Zhang, and Junyi Zhang for their groundbreaking research on Trojan virus detection and classification as published in their article titled "Trojan virus detection and classification based on graph convolutional neural network algorithm"[32]. Their innovative application of graph convolutional networks has significantly influenced my understanding of advanced techniques in malware detection and has provided valuable inspiration for my own research in cross-modal representation learning. I would also like to express my heartfelt appreciation to Xingpeng Xiao, Yaomin Zhang, Jian Xu, Wenkun Ren, and Junyi Zhang for their important study on data leakage risks in large language models, as published in their article titled "Assessment Methods and Protection Strategies for Data Leakage Risks in Large Language Models"[33]. Their comprehensive analysis of data security challenges has enhanced my knowledge of model robustness and inspired aspects of my research on maintaining representation integrity across environmental conditions.

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