Open-Vocabulary Remote Sensing Image Segmentation (OVRSIS), an emerging task that adapts Open-Vocabulary Segmentation (OVS) to the remote sensing (RS) domain, remains underexplored due to the absence of a unified evaluation benchmark and the domain gap between natural and RS images. To bridge these gaps, we first establish a standardized OVRSIS benchmark (OVRSISBench) based on widely-used RS segmentation datasets, enabling consistent evaluation across methods. Using this benchmark, we comprehensively evaluate several representative OVS/OVRSIS models and reveal their limitations when directly applied to remote sensing scenarios. Building on these insights, we propose RSKT-Seg, a novel open-vocabulary segmentation framework tailored for remote sensing. RSKT-Seg integrates three key components: (1) a Multi-Directional Cost Map Aggregation (RS-CMA) module that captures rotation-invariant visual cues by computing vision-language cosine similarities across multiple directions; (2) an Efficient Cost Map Fusion (RS-Fusion) transformer, which jointly models spatial and semantic dependencies with a lightweight dimensionality reduction strategy; and (3) a Remote Sensing Knowledge Transfer (RS-Transfer) module that injects pre-trained knowledge and facilitates domain adaptation via enhanced upsampling. Extensive experiments on the benchmark show that RSKT-Seg consistently outperforms strong OVS baselines by +3.8 mIoU and +5.9 mACC, while achieving 2× faster inference through efficient aggregation.

Time series analysis plays a vital role in fields such as finance, healthcare, industry, and meteorology, underpinning key tasks including classification, forecasting, and anomaly detection. Although deep learning models have achieved remarkable progress in these areas in recent years, constructing an efficient, multi-task compatible, and generalizable unified framework for time series analysis remains a significant challenge. Existing approaches are often tailored to single tasks or specific data types, making it difficult to simultaneously handle multi-task modeling and effectively integrate information across diverse time series types. Moreover, real-world data are often affected by noise, complex frequency components, and multi-scale dynamic patterns, which further complicate robust feature extraction and analysis. To ameliorate these challenges, we propose FusAD, a unified analysis framework designed for diverse time series tasks. FusAD features an adaptive time-frequency fusion mechanism, integrating both Fourier and Wavelet transforms to efficiently capture global-local and multi-scale dynamic features. With an adaptive denoising mechanism, FusAD automatically senses and filters various types of noise, highlighting crucial sequence variations and enabling robust feature extraction in complex environments. In addition, the framework integrates a general information fusion and decoding structure, combined with masked pre-training, to promote efficient learning and transfer of multi-granularity representations. Extensive experiments demonstrate that FusAD consistently outperforms state-of-the-art models on mainstream time series benchmarks for classification, forecasting, and anomaly detection tasks, while maintaining high efficiency and scalability. Code is available at https://github.com/zhangda1018/FusAD.

Vision-Language Models (VLMs) such as CLIP have demonstrated outstanding performance in cross-modal tasks, but the prohibitive computational cost hinders practical deployment. Although Knowledge Distillation (KD) provides a promising compression paradigm, most existing methods rely heavily on feature imitation and contrastive relations without explicit fine-grained alignment. Additionally, they do not fully leverage the multimodal interaction knowledge from the teacher model, restricting cross-modal semantic alignment. To address these challenges, we propose KAID, a Knowledge-Aware Interactive Distillation method for VLMs. Specifically, we first pretrain a large CLIP teacher model with domain few-shot labels and store text features as category vectors. Then, an Image Feature Matching (IFM) module is introduced to calculate the feature distribution of teacher-student models with improved cosine similarity, which achieves hierarchical knowledge transfer from global to local levels and enhances the fine-grained perception of student model through joint optimization. Moreover, a Pixel-Wise Alignment (PWA) module is constructed between the teacher's text features and the student's image features, employing a cross-modal attention mechanism to establish semantic associations, while a Text-guided Pixel alignment Loss function (TPloss) is concurrently designed to enhance the student's comprehension capabilities. Ultimately, the well-trained student model is used for inference. Extensive experiments on 11 datasets validate the effectiveness of our method. Specifically, our method achieves average improvements of 2.14% and 2.40% on the base and new classes across these datasets.

Multimodal semantic segmentation shows significant potential for enhancing segmentation accuracy in complex scenes. However, current methods often incorporate specialized feature fusion modules tailored to specific modalities, thereby restricting input flexibility and increasing the number of training parameters. To address these challenges, we propose StitchFusion, a straightforward yet effective modal fusion framework that integrates large-scale pre-trained models directly as encoders and feature fusers. This approach facilitates comprehensive multi-modal and multi-scale feature fusion, accommodating any visual modal inputs. Specifically, our framework achieves modal integration during encoding by sharing multi-modal visual information. To enhance information exchange across modalities, we introduce a multi-directional Modality Adapter module (MoA) to enable cross-modal information transfer during encoding. By leveraging MoA to propagate multi-scale information across pre-trained encoders during the encoding process, StitchFusion achieves multi-modal visual information integration during encoding. Extensive comparative experiments demonstrate that our model achieves state-of-the-art performance on four multi-modal segmentation datasets with minimal additional parameters. Furthermore, the experimental integration of MoA with existing Feature Fusion Modules (FFMs) highlights their complementary nature. Our anonymous code is https://anonymous.4open.science/r/StitchFusion_V2-E777