With the rapid advancement of vision-language models (VLMs) in general-purpose settings, their application to cross-modal retrieval and semantic understanding of large-scale multimodal remote sensing (RS) data is emerging as a key enabler for urban governance, environmental monitoring, and disaster response. However, the pervasive issue of semantic shift in RS image poses a significant challenge to the transferability of pre-trained VLMs. To address this limitation, we propose ReCoTR, an enhanced CLIP-based cross-modal retrieval framework tailored for remote sensing applications. ReCoTR tackles region-level granularity bias and contextual semantic drift through a Dual Consensus Token Evaluation (DCTE) module, which leverages a mixture-of-experts strategy to fuse inter-modal semantic consensus with intra-modal structural consistency, enabling fine-grained estimation of semantic confidence for visual tokens. Moreover, to mitigate representational contamination caused by background noise, we introduce the Semantic Confidence Token Compression (SCTC) module. This module selectively filters and aggregates tokens with high semantic relevance, thus reducing redundancy and alleviating the noise amplification inherent in CLIP's average pooling. Experimental results on three benchmark RS cross-modal retrieval datasets demonstrate that ReCoTR consistently outperforms existing methods on bidirectional image-text retrieval tasks, validating its effectiveness and robustness in remote sensing semantic alignment scenarios. Our source codes are available at: https://github.com/Jerry710/ReCoTR.git.

Detecting AI-synthetic faces presents a critical challenge: it is hard to capture consistent structural relationships between facial regions across diverse generation techniques. Current methods, which focus on specific artifacts rather than fundamental inconsistencies, often fail when confronted with novel generative models. To address this limitation, we introduce Layer-aware Mask Modulation Vision Transformer (LAMM-ViT), a Vision Transformer designed for robust facial forgery detection. This model integrates distinct Region-Guided Multi-Head Attention (RG-MHA) and Layer-aware Mask Modulation (LAMM) components within each layer. RG-MHA utilizes facial landmarks to create regional attention masks, guiding the model to scrutinize architectural inconsistencies across different facial areas. Crucially, the separate LAMM module dynamically generates layer-specific parameters, including mask weights and gating values, based on network context. These parameters then modulate the behavior of RG-MHA, enabling adaptive adjustment of regional focus across network depths. This architecture facilitates the capture of subtle, hierarchical forgery cues ubiquitous among diverse generation techniques, such as GANs and Diffusion Models. In cross-model generalization tests, LAMM-ViT demonstrates superior performance, achieving 94.09% mean ACC (a +5.45% improvement over SoTA) and 98.62% mean AP (a +3.09% improvement). These results demonstrate LAMM-ViT’s exceptional ability to generalize and its potential for reliable deployment against evolving synthetic media threats.The code is available at https://github.com/WHUT-ZJL/LAMM-ViT.

Rapid advances in Earth observation technologies have yielded numerous remotely sensed images and corresponding text data, enabling cross-modal image–text retrieval to extract valuable clues. However, current methods often focus on learning global semantic information from text and remote sensing (RS) images, while neglecting fine-grained semantic alignment and correlation. In addition, contrastive learning between modalities is often insufficient. To address these issues, we propose an innovative cues-driven multiassociation feature matching network (CDMAN) for cross-modal RS image retrieval. The proposed method primarily involves two key steps: 1) aligning positive samples and enhancing fusion for negative samples based on modal cues. To achieve precise alignment between RS images and text and facilitate the learning process for negative samples in contrastive learning, we have developed a novel fine-grained cues injection module that aligns and guides modalities using fine-grained cues; and 2) establishing multigranularity associative learning. To address the issue of insufficient association between RS images and text, we have implemented multigranularity collaborative associative learning, focusing on general and fine-grained modal associations. By fully leveraging modal cues, our method maintains both detailed associations and overall consistency in global associations. Experiments demonstrate that, compared to baseline methods, this approach achieves more accurate cross-modal retrieval (MCR) by combining fine-grained alignment and multigranularity associations.

In recent years, with the continuous advancement of remote sensing technology and text processing techniques, there has been a growing abundance of remote sensing images and associated textual data. Combining remote sensing images with their corresponding textual data allows for integrated analysis and retrieval, which holds significant practical implications across multiple application domains, including geographic information systems (GIS), environmental monitoring, and agricultural management. Remote sensing images have the characteristics of multi-targets and multi-scales, and the textual descriptions of these targets are not fully utilized, leading to a decrease in retrieval accuracy. Previous methods have struggled to balance inter-modality information interaction and intra-modality feature fusion, and they have paid little attention to the consistency of distribution within modalities. In light of this, this paper proposes a symmetric multi-level guidance network (SMLGN) for cross-modal retrieval in remote sensing. SMLGN first introduces fusion guidance between local and global within modalities and fine-grained bidirectional guidance between modalities, allowing for the learning of a common semantic space. Furthermore, to address the distribution differences of different modalities within the common semantic space, we design an adversarial joint learning framework and a multi-objective loss function to optimize the SMLGN method and achieve consistency in data distribution. The experimental results demonstrate that the SMLGN method performs well in the task of cross-modal retrieval between remote sensing images and textual data. It effectively integrates the information from both modalities, improving the accuracy and reliability of the retrieval process.

Hyperspectral image change detection (HSI-CD) is a task that utilizes both spectral and spatial features to more effectively detect changes. The features of HSI captured at different times are often influenced by external factors. The annoying variability is not useful for CD. Current methods extract effective information through complex learning together with it. They did not focus on whether different categories (changed and unchanged) of sample pairs have the same information composition. They lack a fundamental analysis and optimization based on the composition of information pairs to address current issues such as insufficient recognition of changed samples and poor feature fusion. To address these issues, we rethink the information composition of bitemporal sample pairs in different categories for HSI-CD, and design a frequency-domain information exchange and generation network with Siamese U-shaped structure (FDIEG-UNet) for HSI-CD. Our contribution can be summarized as follows: 1) by designing the FDGJLS module for learning and separating global-joint spectral-spatial features, to provide better basic frequency-domain information for subsequent feature-domain optimization; 2) based on the information from FDGJLS, we use the ULDT and SSTDFE modules to remove the influence of temporal-correlated invalid style information in feature fusion. Two modules work together to improve the equality of feature description between changed and unchanged samples in CD; and 3) the experimental results on three real HSI-CD datasets demonstrate the effectiveness of our proposed method in solving the above problems. The code is available athttps://github.com/WUTCM-Lab/FDIEG-UNet.