Few-shot learning (FSL) aims to recognize new concepts using a limited number of visual samples. Existing methods attempt to incorporate semantic information into the limited visual data for category understanding. However, these methods often enrich class-level feature representations with abstract category names, failing to capture nuanced features essential for effective generalization. To address this issue, we propose a novel framework for FSL, which incorporates both the abstract class semantics and the concrete class entities extracted from Large Language Models (LLMs), to enhance the representation of the class prototypes. Specifically, our framework composes a Semantic-guided Visual Pattern Extraction (SVPE) module and a Prototype-Calibration (PC) module, where the SVPE meticulously extracts semantic-aware visual patterns across diverse scales, while the PC module seamlessly integrates these patterns to refine the visual prototype, enhancing its representativeness. Extensive experiments on four few-shot classification benchmarks and the BSCD-FSL cross-domain benchmark showcase remarkable advancements over the current state-of-the-art methods. Notably, for the challenging one-shot setting, our approach, utilizing the ResNet-12 backbone, achieves an impressive average improvement of 1.95% over the second-best competitor.

Due to the large-scale image size and object variations, current CNN-based and Transformer-based approaches for remote sensing image semantic segmentation are suboptimal for capturing the long-range dependency or limited to the complex computational complexity. In this paper, we propose CM-UNet, comprising a CNN-based encoder for extracting local image features and a Mamba-based decoder for aggregating and integrating global information, facilitating efficient semantic segmentation of remote sensing images. Specifically, a CSMamba block is introduced to build the core segmentation decoder, which employs channel and spatial attention as the gate activation condition of the vanilla Mamba to enhance the feature interaction and global-local information fusion. Moreover, to further refine the output features from the CNN encoder, a Multi-Scale Attention Aggregation (MSAA) module is employed to merge the different scale features. By integrating the CSMamba block and MSAA module, CM-UNet effectively captures the long-range dependencies and multi-scale global contextual information of large-scale remote-sensing images. Experimental results obtained on three benchmarks indicate that the proposed CM-UNet outperforms existing methods in various performance metrics. The codes are available at https://github.com/XiaoBuL/CM-UNet.

Recently, methods that utilize prompt tuning to rapidly transfer pretrained vision-language models (VLMs) to downstream tasks have been proposed. Although these models have produced reasonable results, they typically learn a single prompt, which limits their ability to capture more diverse information. This ability is crucial for addressing fine-grained classification challenges and intraclass visual variability (e.g., color, pose, and size variations within the same category). However, learning multiple prompts provides a larger optimization space, which further exacerbates the overfitting phenomenon. This makes it more challenging balance the performances acienved for base and new categories. To address these issues, we propose progressive multi-prompt (PMP) learning method.ecently, methods that utilize prompt tuning to rapidly transfer pretrained vision-language models (VLMs) to downstream tasks have been proposed. Although these models have produced reasonable results, they typically learn a single prompt, which limits their ability to capture more diverse information. This ability is crucial for addressing fine-grained classification challenges and intraclass visual variability (e.g., color, pose, and size variations within the same category). However, learning multiple prompts provides a larger optimization space, which further exacerbates the overfitting phenomenon. This makes it more challenging balance the performances acienved for base and new categories. To address these issues, we propose progressive multiprompt (PMP) learning method.R Specifically, we introduce multiple prompts in a step-by-step manner to focus on various information. To reduce overfitting, we utilize a late attaching mechanism to defer the interactions of prompts and features to a deeper encoding layer. Furthermore, we balance the prompts for different layers with learnable weights to guide the optimal optimization procedure. We compared our method with several state-of-the-art approaches in base-to-new task settings and demonstrate superior base-new tradeoff performance. Additionally, we conducted cross-dataset transfer, domain generalization, and few-shot experiments to further validate the effectiveness of our method. Our code is available at https://github.com/JunLGeek/PMP.git.