Few-Shot Image Classification

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 41

Apr 26, 2026cs.CV

VDLF-Net: Variational Feature Fusion for Adaptive and Few-Shot Visual Learning

This paper introduces VDLF-Net, which attaches a compact VAE to a multi-scale CNN backbone. Latent vectors and softmax-gate support the backbone feature maps, while

\ell_2

-normalized embeddings from the gated maps contribute toward supervised classification or episodic few-shot prediction. Under standard CIFAR-100 and Mini-ImageNet protocols, VDLF-Net demonstrates an improved performance over ResNet-50 Enhanced, VGG-16, Prototypical Networks, and Matching Networks. Extensive ablations show that removing the fine-resolution scale has the greatest impact on VDLF-Net's performance. At the same time, KL and reconstruction at the chosen

α

pose a minor performance reduction, demonstrating that performance gains over classical episodic baselines mainly originate from the full VDLF-Net architecture and training strategy.

Jiawei Yan

Department of Statistics, Shanghai Jiao Tong University, Shanghai, 200240, China.

Multi-Scale Feature Fusion Convolutional Neural Networks Few-Shot Image Classification

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Apr 22, 2026cs.NE

Where to Bind Matters: Hebbian Fast Weights in Vision Transformers for Few-Shot Character Recognition

Standard transformer architectures learn fixed slow-weight representations during training and lack mechanisms for rapid adaptation within an episode. In contrast, biological neural systems address this through fast synaptic updates that form transient associative memories during inference, a property known as Hebbian plasticity. In this paper, we conduct an empirical study of Hebbian Fast-Weight (HFW) modules integrated into multiple transformer backbones, including ViT-Small, DeiT-Small, and Swin-Tiny. We evaluate six model variants: ViT, DeiT, Swin, ViT-Hebbian, DeiT-Hebbian, and Swin-Hebbian on 5-way 1-shot and 5-way 5-shot classification tasks using the Omniglot benchmark under a Prototypical Network meta-learning framework. We propose a single module placement strategy for Swin-Tiny in which one HFW module is applied to the final stage feature map after all hierarchical stages have completed. This design avoids the training instability caused by placing separate Hebbian modules at each stage and achieves the highest test accuracy across all six models (96.2% at 1-shot; 99.2% at 5-shot), outperforming its non-Hebbian baseline by

+0.3

percentage points at 1-shot. We analyze the interaction between Swin's shifted window inductive bias and episode-level Hebbian binding, discuss why per-block placement fails for ViT and DeiT variants in a low-data regime, and situate the results within the wider literature on fast and slow-weight meta-learning.

Gavin Money, Sindhuja Penchala, Jiacheng Li +1

Department of Computer Science, The University of Alabama, Tuscaloosa, AL, USA

Hebbian Learning Vision Transformer Few-Shot Image Classification

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Apr 18, 2026cs.CV

Adaptive receptive field-based spatial-frequency feature reconstruction network for few-shot fine-grained image classification

Feature reconstruction techniques are widely applied for few-shot fine-grained image classification (FSFGIC). Our research indicates that one of the main challenges facing existing feature-based FSFGIC methods is how to choose the size of the receptive field to extract feature descriptors (including spatial and frequency feature descriptors) from different category input images, thereby better performing the FSFGIC tasks. To address this, an adaptive receptive field-based spatial-frequency feature reconstruction network (ARF-SFR-Net) is proposed. The designed ARF-SFR-Net has the capability to adaptively determine receptive field sizes for obtaining spatial and frequency features, and effectively fuse them for reconstruction and FSFGIC tasks. The designed ARF-SFR-Net can be easily embedded into a given episodic training mechanism for end-to-end training from scratch. Extensive experiments on multiple FSFGIC benchmarks demonstrate the effectiveness and superiority of the proposed ARF-SFR-Net over state-of-the-art approaches. The code is available at: https://github.com/ICL-SUST/ARF-SFR-Net.git.

Linyue Zhang, Wenyi Zeng, Zicheng Pan +6

School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, Shaanxi Province, China · School of Electrical Engineering and Computer Science, the University of Queensland, QLD, Australia · Institute for Integrated and Intelligent Systems, Griffith University, QLD, Australia +4

Frequency-Domain Feature Learning Fine-Grained Image Classification Few-Shot Image Classification Fine-Grained Visual Perception

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Apr 16, 2026cs.CV

Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification

Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples. Existing metric learning-based methods typically rely solely on spatial domain features. Confined to this single perspective, models inevitably suffer from inherent texture biases, entangling essential structural details with high-frequency background noise. Furthermore, lacking cross-view geometric constraints, single-view metrics tend to overfit this noise, resulting in structural instability under few-shot conditions. To address these issues, this paper proposes the Frequency-Enhanced Dual-Subspace Network (FEDSNet). Specifically, FEDSNet utilizes the Discrete Cosine Transform (DCT) and a low-pass filtering mechanism to explicitly isolate low-frequency global structural components from spatial features, thereby suppressing background interference. Truncated Singular Value Decomposition (SVD) is employed to construct independent, low-rank linear subspaces for both spatial texture and frequency structural features. An adaptive gating mechanism is designed to dynamically fuse the projection distances from these dual views. This strategy leverages the structural stability of the frequency subspace to prevent the spatial subspace from overfitting to background features. Extensive experiments on four benchmark datasets - CUB-200-2011, Stanford Cars, Stanford Dogs, and FGVC-Aircraft - demonstrate that FEDSNet exhibits excellent classification performance and robustness, achieving highly competitive results compared to existing metric learning algorithms. Complexity analysis further confirms that the proposed network achieves a favorable balance between high accuracy and computational efficiency, providing an effective new paradigm for few-shot fine-grained visual recognition.

Meijia Wang, Guochao Wang, Haozhen Chu +4

School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China

Frequency-Domain Feature Learning Fine-Grained Image Classification Metric Learning Few-Shot Image Classification Fine-Grained Visual Perception

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Apr 16, 2026cs.CV

Chaotic CNN for Limited Data Image Classification

Convolutional neural networks (CNNs) often exhibit poor generalisation in limited training data scenarios due to overfitting and insufficient feature diversity. In this work, a simple and effective chaos-based feature transformation is proposed to enhance CNN performance without increasing model complexity. The method applies nonlinear transformations using logistic, skew tent, and sine maps to normalised feature vectors before the classification layer, thereby reshaping the feature space and improving class separability. The approach is evaluated on greyscale datasets (MNIST and Fashion-MNIST) and an RGB dataset (CIFAR-10) using CNN architectures of varying depth under limited data conditions. The results show consistent improvement over the standalone (SA) CNN across all datasets. Notably, a maximum performance gain of 5.43% is achieved on MNIST using the skew tent map with a 3-layer CNN at 40 samples per class. A higher gain of 9.11% is observed on Fashion-MNIST using the sine map with a 3-layer CNN at 50 samples per class. Additionally, a strong gain of 7.47% is obtained on CIFAR-10 using the skew tent map at 200 samples per class. The consistent improvements across different chaotic maps indicate that the performance gain is driven by the shared nonlinear and dynamical properties of chaotic systems. The proposed method is computationally efficient, requires no additional trainable parameters, and can be easily integrated into existing CNN architectures, making it a practical solution for data-scarce image classification tasks.

Anusree M, Akhila Henry, Pramod P Nair

1*Department of Mathematics, Amrita Vishwa Vidyapeetham, Amritapuri, India.

Neural Network Generalization Convolutional Neural Networks Few-Shot Image Classification Data-Efficient Learning

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Mar 25, 2026cs.CV

Project and Mix: Task-Semantic Prototypes for Few-Shot Image Classification

Vision-language models like CLIP are trained with the objective of aligning text and image pairs. Beyond text prompts alone, recent works show that exploiting few-shot image embeddings from a training set is effective for CLIP-based classification. In this work, we analyze mixing image and text prototypes from a bias-variance perspective and show that mixing prototypes acts like a variance shrinkage estimator. Naively mixing text and image prototypes combines two partially aligned spaces since the two modalities are not perfectly aligned. To address this, we project image prototypes onto the principal directions of the semantic text embedding space to obtain a task-semantic image subspace. Mixing the image prototypes with text embeddings in the task-semantic subspace improves few-shot classification. However, when the task-semantic subspace captures insufficient discriminative visual information, relying on this subspace alone can be suboptimal. On extensive experiments over several few-shot classification benchmarks, we show that combining a task-semantic mixed prototype classifier and an anisotropic image-specific classifier systematically outperforms existing methods.

Dipam Goswami, Simone Magistri, Gido M. van de Ven +4

Department of Computer Science, Universitat Autònoma de Barcelona, Spain · Computer Vision Center, Barcelona, Spain · Media Integration and Communication Center, University of Florence, Italy +3

Prototype-Based Learning VLM Adaptation Vision-Language Alignment Few-Shot Image Classification

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Dec 10, 2025cs.LG

Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors

Visual Species Recognition (VSR) is a fundamental task in scientific disciplines that require species-level identification, including ecology, palynology, evolutionary biology, systematics, and phylogenetics. Automating VSR through machine learning can significantly accelerate these efforts. However, species-level annotation requires extensive domain expertise, making large-scale labeled datasets difficult to obtain. Consequently, few-shot learning (FSL) is a practical paradigm, where an expert model is trained using only a few labeled examples. Meanwhile, Large Multimodal Models (LMMs) have demonstrated unprecedented zero-shot visual recognition capabilities, raising the question of whether they can serve as an alternative to FSL expert models for VSR. We start this work with a systematic comparison between FSL expert models and LMMs, revealing that, despite advanced prompting strategies, contemporary LMMs significantly underperform FSL expert models. Interestingly, we find that LMMs possess a complementary strength: given an image and a shortlist of candidate species generated by an expert model, LMMs can often recover the correct label when the expert model's top prediction is incorrect. Motivated by this, we propose Post-hoc Correction (POC), a simple training-free framework that leverages an LMM to post-process an expert model's top predictions. We develop a multimodal prompting strategy to enable POC to improve FSL expert models by 6.4 accuracy points, averaged over five VSR benchmarks. We show that POC generalizes across diverse FSL methods, visual encoders, and LMMs, making it a practical and effective framework for VSR.

Tian Liu, Anwesha Basu, James Caverlee +1

Department of Computer Science and Engineering, Texas A&M University · Department of AI, University of Macau

Large Vision-Language Models Multimodal Large Language Models Few-Shot Image Classification Image Classification

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Sep 30, 2025cs.CV

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples

AI-generated image (AIGI) attribution presents a pressing challenge that goes beyond mere AIGI detection, aiming to identify the source model or technique responsible for a synthetic image. However, most previous source attribution methods operate in a closed-set manner, which necessitates retraining to recognize any novel category, preventing adaptation to the rapid evolution of image generation. In this work, we propose a new paradigm for synthetic image attribution, termed few-shot attribution. This paradigm targets the reliable identification of unseen generators using only limited samples, making it highly suitable for real-world applications. To facilitate this work, we construct OmniFake, a large-scale, well-categorized synthetic image dataset that contains

1.17

million images from

45

distinct generators. We further introduce OmniDFA (Omni Detector and Few-shot Attributor), a few-shot attribution baseline that not only assesses the authenticity of images but also determines their synthesis origins. Experiments demonstrate that OmniDFA exhibits excellent capability in few-shot attribution and achieves state-of-the-art generalization performance in AIGI detection. Our dataset and code are available at https://github.com/teheperinko541/OmniDFA.

Shiyu Wu, Shuyan Li, Jing Li +2

Institute of Automation, Chinese Academy of Sciences, Beijing, China · Beijing Academy of Artificial Intelligence, Beijing, China · University of Chinese Academy of Sciences, Beijing, China +3

Few-Shot Learning AI-Generated Image Detection Few-Shot Image Classification

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Jul 8, 2024cs.CV

Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning

Few-shot class-incremental learning (FSCIL) aims to incrementally learn novel classes from limited examples while preserving knowledge of previously learned classes. Existing methods face a critical dilemma: static architectures rely on a constant parameter space to learn from data that arrive sequentially, making them prone to overfitting to the current session, while dynamic architectures continually expand the parameter space, leading to increased complexity. In this study, we explore the potential of Selective State Space Models (SSMs) for FSCIL. Mamba leverages its input-dependent parameters to dynamically adjust its processing patterns and generate content-aware scan patterns without session-wise projector expansion. This enables it to configure distinct processing for base and novel classes, helping preserve existing knowledge while adapting to new ones. To leverage Mamba's potential for FSCIL, we design two key modules: First, we propose a dual selective SSM projector that generates input-conditioned state-space parameters from intermediate features for dynamic adaptation. The dual design structurally decouples base and novel-class processing, employing a frozen base branch to maintain stable base-class features and a dynamic incremental branch that adaptively learns distinctive feature shifts for novel classes. Second, we develop a class-sensitive selective scan mechanism to guide dynamic adaptation of the incremental branch. It reduces the disruption to base-class representations caused by training on novel data, and meanwhile, encourages the selective scan to perform in distinct patterns between base and novel classes. Extensive experiments on miniImageNet, CIFAR-100, and CUB-200 demonstrate that Mamba-FSCIL achieves state-of-the-art performance.

Xiaojie Li, Yibo Yang, Jianlong Wu +4

School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China · Pengcheng Laboratory, Shenzhen, China · King Abdullah University of Science and Technology, Jeddah, Saudi Arabia +1

Class-Incremental Learning Incremental Learning Few-Shot Image Classification State Space Models

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Date pendingcs.CV

Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classification

Few-shot fine-grained image classification (FSFGIC) aims to classify similar images with limited labeled examples. This work highlights the critical yet underutilized role of phase information in capturing structural relationships within an image. This study introduces a novel plug-and-play amplitude-phase integration (API) module that effectively combines local and global frequency amplitude and phase information for obtaining more comprehensive feature descriptors. Additionally, a dedicated network, named PSF-Net, is proposed that adaptively fuses phase-based spatial and frequency information for FSFGIS. The designed PSF-Net can be easily integrated into standard episodic training architectures for end-to-end training from scratch. Extensive experiments on five public datasets demonstrate that the method outperforms existing state-of-the-art benchmarks.

Ruiling Liu, Linyue Zhang, Wenyi Zeng +5

Frequency-Domain Feature Learning Fine-Grained Image Classification Few-Shot Image Classification Fine-Grained Visual Perception

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Date pendingcs.CV

Are We Really Doing Few-Shot Learning? A Critical Examination of Pre-Training Assumptions

Few-shot learning is commonly evaluated under protocols that pre-train a model on a large auxiliary set whose classes are disjoint from the target episodes yet drawn from the same visual domain. This paper examines whether such protocols truly reflect low-data learning. We systematically compare no pre-training, class-disjoint in-domain pre-training, supervised out-of-domain pre-training, and label-free out-of-domain pre-training across eight datasets, three few-shot architectures, and multiple way-shot settings. Our results show that class disjointness alone is insufficient to remove the influence of target-domain data. In-domain pre-training improves over no pre-training by 33.41 percentage points on average, whereas supervised out-of-domain pre-training yields 23.75 percentage points, revealing a 9.66-point optimistic bias associated with domain overlap. Although out-of-domain pre-training is more realistic in applications where target-domain data are scarce, its effectiveness depends strongly on the compatibility between source and target domains. We further show that labeled source data are not strictly required, with an augmentation-based label-free strategy reaching an average gain of 27.71 percentage points and closely matching supervised out-of-domain pre-training at 27.97 percentage points. Finally, we introduce a descriptor-based source-selection strategy that estimates source-domain suitability before pre-training, reaching a median gap of only 1.37 percentage points to oracle selection. These findings highlight the need to move beyond in-domain pre-training as the default few-shot evaluation protocol, since it can overestimate performance in realistic scenarios where target-domain data are scarce.

Alejandro Galan-Cuenca, Marcelo Saval-Calvo, Antonio Javier Gallego

University Institute for Computer Research, University of Alicante, Alicante, Spain

Domain Adaptation Few-Shot Learning Few-Shot Image Classification

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