  Semantic Communication (SC) has emerged as a novel communication paradigm in
recent years, successfully transcending the Shannon physical capacity limits
through innovative semantic transmission concepts. Nevertheless, extant Image
Semantic Communication (ISC) systems face several challenges in dynamic
environments, including low semantic density, catastrophic forgetting, and
uncertain Signal-to-Noise Ratio (SNR). To address these challenges, we propose
a novel Vision-Language Model-based Cross-modal Semantic Communication
(VLM-CSC) system. The VLM-CSC comprises three novel components: (1) Cross-modal
Knowledge Base (CKB) is used to extract high-density textual semantics from the
semantically sparse image at the transmitter and reconstruct the original image
based on textual semantics at the receiver. The transmission of high-density
semantics contributes to alleviating bandwidth pressure. (2) Memory-assisted
Encoder and Decoder (MED) employ a hybrid long/short-term memory mechanism,
enabling the semantic encoder and decoder to overcome catastrophic forgetting
in dynamic environments when there is a drift in the distribution of semantic
features. (3) Noise Attention Module (NAM) employs attention mechanisms to
adaptively adjust the semantic coding and the channel coding based on SNR,
ensuring the robustness of the CSC system. The experimental simulations
validate the effectiveness, adaptability, and robustness of the CSC system.
