MPA: Multimodal Prototype Augmentation for Few-Shot Learning

• URL: http://arxiv.org/abs/2602.10143v1
• Date: Mon, 09 Feb 2026 08:30:31 GMT
• Title: MPA: Multimodal Prototype Augmentation for Few-Shot Learning
• Authors: Liwen Wu, Wei Wang, Lei Zhao, Zhan Gao, Qika Lin, Shaowen Yao, Zuozhu Liu, Bin Pu,
• Abstract summary: Few-shot learning has become a popular task that aims to recognize new classes from only a few labeled examples.<n>We propose a novel framework called MPA, including Multi-Variant Semantic Enhancement (LMSE), Hierarchical Multi-View Augmentation (HMA), and an Adaptive Uncertain Class Absorber (AUCA)<n> MPA achieves superior performance compared to existing state-of-the-art methods across most settings.
• Score: 36.74394076733568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, few-shot learning (FSL) has become a popular task that aims to recognize new classes from only a few labeled examples and has been widely applied in fields such as natural science, remote sensing, and medical images. However, most existing methods focus only on the visual modality and compute prototypes directly from raw support images, which lack comprehensive and rich multimodal information. To address these limitations, we propose a novel Multimodal Prototype Augmentation FSL framework called MPA, including LLM-based Multi-Variant Semantic Enhancement (LMSE), Hierarchical Multi-View Augmentation (HMA), and an Adaptive Uncertain Class Absorber (AUCA). LMSE leverages large language models to generate diverse paraphrased category descriptions, enriching the support set with additional semantic cues. HMA exploits both natural and multi-view augmentations to enhance feature diversity (e.g., changes in viewing distance, camera angles, and lighting conditions). AUCA models uncertainty by introducing uncertain classes via interpolation and Gaussian sampling, effectively absorbing uncertain samples. Extensive experiments on four single-domain and six cross-domain FSL benchmarks demonstrate that MPA achieves superior performance compared to existing state-of-the-art methods across most settings. Notably, MPA surpasses the second-best method by 12.29% and 24.56% in the single-domain and cross-domain setting, respectively, in the 5-way 1-shot setting.

Related papers

• Multi-Paradigm Collaborative Adversarial Attack Against Multi-Modal Large Language Models [67.45032003041399]
  We propose a novel Multi-Paradigm Collaborative Attack (MPCAttack) framework to boost the transferability of adversarial examples against MLLMs.<n>MPCO adaptively balances the importance of different paradigm representations and guides the global optimisation.<n>Our solution consistently outperforms state-of-the-art methods in both targeted and untargeted attacks on open-source and closed-source MLLMs.
  arXiv  Detail & Related papers  (2026-03-05T06:01:26Z)
• Detecting Deepfakes with Multivariate Soft Blending and CLIP-based Image-Text Alignment [4.34685509565816]
  The proliferation of highly realistic facial forgeries necessitates robust detection methods.<n>Existing approaches often suffer from limited accuracy and poor generalization due to significant distribution shifts among samples generated by diverse forgery techniques.<n>Our method leverages the multimodal alignment capabilities of CLIP to capture subtle forgery traces.
  arXiv  Detail & Related papers  (2026-02-14T09:53:35Z)
• Importance Sampling for Multi-Negative Multimodal Direct Preference Optimization [68.64764778089229]
  We propose MISP-DPO, the first framework to incorporate multiple, semantically diverse negative images in multimodal DPO.<n>Our method embeds prompts and candidate images in CLIP space and applies a sparse autoencoder to uncover semantic deviations into interpretable factors.<n>Experiments across five benchmarks demonstrate that MISP-DPO consistently improves multimodal alignment over prior methods.
  arXiv  Detail & Related papers  (2025-09-30T03:24:09Z)
• Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs [78.09559830840595]
  We present the first systematic study on quantizing diffusion-based language models.<n>We identify the presence of activation outliers, characterized by abnormally large activation values.<n>We implement state-of-the-art PTQ methods and conduct a comprehensive evaluation.
  arXiv  Detail & Related papers  (2025-08-20T17:59:51Z)
• Multi-Prompt Progressive Alignment for Multi-Source Unsupervised Domain Adaptation [73.40696661117408]
  We propose a progressive alignment strategy for adapting CLIP to unlabeled downstream task.<n>We name our approach MP2A and test it on three popular UDA benchmarks, namely ImageCLEF, Office-Home, and the most challenging DomainNet.<n> Experiments showcase that MP2A achieves state-of-the-art performance when compared with most recent CLIP-based MS-UDA approaches.
  arXiv  Detail & Related papers  (2025-07-31T09:42:42Z)
• TAMP: Token-Adaptive Layerwise Pruning in Multimodal Large Language Models [23.916205754112774]
  Multimodal Large Language Models (MLLMs) have shown remarkable versatility in understanding diverse multimodal data and tasks.<n>We propose TAMP, a simple yet effective pruning framework tailored for MLLMs.<n>We validate our method on two state-of-the-art MLLMs: LLaVA-NeXT, designed for vision-language tasks, and VideoLLaMA2, capable of processing audio, visual, and language modalities.
  arXiv  Detail & Related papers  (2025-04-14T05:44:38Z)
• Multi-scale Activation, Refinement, and Aggregation: Exploring Diverse Cues for Fine-Grained Bird Recognition [35.99227153038734]
  Fine-Grained Bird Recognition (FGBR) has gained increasing attention.<n>Recent studies reveal that the limited receptive field of plain ViT model hinders representational richness.<n>We propose a novel framework for FGBR, namely Multi-scale Diverse Cues Modeling (MDCM)
  arXiv  Detail & Related papers  (2025-04-12T13:47:24Z)
• Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting [9.116108409344177]
  The source-free cross-domain few-shot learning task aims to transfer pretrained models to target domains utilizing minimal samples.<n>We propose the SeGD-VPT framework, which is divided into two phases.<n>The first step aims to increase feature diversity by adding diversity prompts to each support sample, thereby generating varying input and enhancing sample diversity.
  arXiv  Detail & Related papers  (2024-12-01T11:00:38Z)
• FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data [56.08867996209236]
  Fine-tuning Multimodal Large Language Models (MLLMs) with Federated Learning (FL) allows for expanding the training data scope by including private data sources.<n>We introduce a benchmark to evaluate the performance of federated fine-tuning of MLLMs across various multimodal heterogeneous scenarios.<n>We develop a general FedMLLM framework that integrates classic FL methods alongside two modality-agnostic strategies.
  arXiv  Detail & Related papers  (2024-11-22T04:09:23Z)
• MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild [81.32127423981426]
  Multimodal emotion recognition based on audio and video data is important for real-world applications. Recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders. We propose a different perspective on the problem and investigate the advancement of multimodal DFER performance by adapting SSL-pre-trained disjoint unimodal encoders.
  arXiv  Detail & Related papers  (2024-04-13T13:39:26Z)
• Exploring Multi-Timestep Multi-Stage Diffusion Features for Hyperspectral Image Classification [16.724299091453844]
  Diffusion-based HSI classification methods only utilize manually selected single-timestep single-stage features. We propose a novel diffusion-based feature learning framework that explores Multi-Timestep Multi-Stage Diffusion features for HSI classification for the first time, called MTMSD. Our method outperforms state-of-the-art methods for HSI classification, especially on the challenging Houston 2018 dataset.
  arXiv  Detail & Related papers  (2023-06-15T08:56:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.