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.

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VDLF-Net: Variational Feature Fusion for Adaptive and Few-Shot Visual Learning
Apr 26, 2026Jiawei YanMulti-Scale Feature Fusion Convolutional Neural Networks
Department of Statistics, Shanghai Jiao Tong University, Shanghai, 200240, China.
Where to Bind Matters: Hebbian Fast Weights in Vision Transformers for Few-Shot Character Recognition
Apr 22, 2026Gavin Money, Sindhuja Penchala, Jiacheng Li +1Hebbian Learning Vision Transformer
Department of Computer Science, The University of Alabama, Tuscaloosa, AL, USA
Adaptive receptive field-based spatial-frequency feature reconstruction network for few-shot fine-grained image classification
Apr 18, 2026Linyue Zhang, Wenyi Zeng, Zicheng Pan +6Frequency-Domain Feature Learning Fine-Grained Image Classification
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-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification
Apr 16, 2026Meijia Wang, Guochao Wang, Haozhen Chu +4Frequency-Domain Feature Learning Fine-Grained Image Classification
School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China
Chaotic CNN for Limited Data Image Classification
Apr 16, 2026Anusree M, Akhila Henry, Pramod P NairNeural Network Generalization Convolutional Neural Networks
1*Department of Mathematics, Amrita Vishwa Vidyapeetham, Amritapuri, India.
Project and Mix: Task-Semantic Prototypes for Few-Shot Image Classification
Mar 25, 2026Dipam Goswami, Simone Magistri, Gido M. van de Ven +4Prototype-Based Learning VLM Adaptation
Department of Computer Science, Universitat Autònoma de Barcelona, Spain · Computer Vision Center, Barcelona, Spain · Media Integration and Communication Center, University of Florence, Italy +3
Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors
Dec 10, 2025Tian Liu, Anwesha Basu, James Caverlee +1Large Vision-Language Models Multimodal Large Language Models
Department of Computer Science and Engineering, Texas A&M University · Department of AI, University of Macau
Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples
Sep 30, 2025Shiyu Wu, Shuyan Li, Jing Li +2Few-Shot Learning AI-Generated Image Detection
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
Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning
Jul 8, 2024Xiaojie Li, Yibo Yang, Jianlong Wu +4Class-Incremental Learning Incremental Learning
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
Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classification
Date pendingRuiling Liu, Linyue Zhang, Wenyi Zeng +5Frequency-Domain Feature Learning Fine-Grained Image Classification
Are We Really Doing Few-Shot Learning? A Critical Examination of Pre-Training Assumptions
Date pendingAlejandro Galan-Cuenca, Marcelo Saval-Calvo, Antonio Javier GallegoDomain Adaptation Few-Shot Learning
University Institute for Computer Research, University of Alicante, Alicante, Spain

Apr 26, 2026, cs.CVJ/K move · Enter open · S save

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

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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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.

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