Image Classification

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Overview

Image classification studies how to assign one or more labels to an image, supporting applications from species identification and medical diagnosis to document processing and remote sensing. Common approaches include convolutional neural networks, vision transformers, and pretrained vision-language models, trained with supervised, self-supervised, transfer, or few-shot learning. Researchers also develop data augmentation, hierarchical prediction, uncertainty estimation, and efficient models for settings such as ImageNet, corruption benchmarks, and specialized domain datasets. A central challenge is that strong accuracy on standard test sets may not transfer under distribution shifts or reveal reliance on spurious cues, while improving robustness, calibration, and efficiency can involve trade-offs.

Latest papers

CardsList

386 papers29 papers in the last four weeks, down 34% on the four weeks before. 0.2% of all new papers.

  1. ACSAN: All-Convolutional Self-Attention Network Integrating Convolutional Operations with Vision Transformer

    Oct 26, 2026Zimian Wei, Tianqi Zhang, Hengyue PanHybrid CNN-Transformer ArchitecturesEfficient ViTs

    College of Computer Science and Technology, National University of Defense Technology, 109 Deya Road, Changsha, 410073, Hunan, China

  2. Component Pooling Trained Across Orientations for Few-Shot Remote Sensing Scene Classification

    Oct 24, 2026Bingchen Yan, Wenheng Guo, Yuye ZhouRemote Sensing Scene ClassificationFew-Shot Image Classification

    School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China · School of Information Engineering, Institute of Disaster Prevention, Langfang, 065201, China · School of Computing & Technology, Asia Pacific University of Technology & Innovation, Kuala Lumpur, 57000, Malaysia

  3. Automated identification of ivory carvings using machine learning: A proof-of-concept approach to detecting the illegal wildlife trade

    Oct 23, 2026Andrius Tamosiunas, Simone Mozzon, David Bacon +2Image Classification

    Institute of Theoretical Astrophysics, University of Oslo, PO Box 1029, Blindern 0315, Norway · CERCA/ISO, Department of Physics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA · Institute of Cosmology and Gravitation, University of Portsmouth, Dennis Sciama Building, Burnaby Road, Portsmouth, PO1 3FX, UK +2

  4. Standing on Open Shoulders: Regional Fine-Tuning of Open-Weight Camera-Trap Models for UK Wildlife with Modest Data and Compute

    Oct 23, 2026Paul FergusImage ClassificationWildlife Monitoring

    Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom

  5. IFC-SSL-Bench: Benchmarking Self-Supervised Learning for Annotation-Efficient Multispectral Imaging Flow Cytometry

    Oct 22, 2026Angelo Jovin Yamachui Sitcheu, Roxana-Teodora Fetescu, Thomas Lautenschlager +6Self-Supervised LearningSelf-Supervised Visual Representation Learning

    Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany · Institute of Biological and Chemical Systems, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany · Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland +5

  6. HullProbe: Measuring Interventional Part Reliance in Maritime Vessel Classifiers

    Oct 18, 2026Faisal Mohammad, Mohamed YounisImage ClassificationFeature Attribution

    University of Maryland, Baltimore County · Johns Hopkins University Applied Physics Laboratory

  7. Approaching Backpropagation Accuracy with Weight Perturbation on MNIST

    Oct 15, 2026Patrick InoueGradient-Free OptimizationNeural Network Optimization

    The MNIST experiments originate from the author’s master’s thesis, submitted under the name Patrick Stricker at Albstadt-Sigmaringen University in March 2023 [4].

  8. CoViG: Coarsened Vision GNN for Image Classification

    Oct 15, 2026Martial Guidez, Stefan Duffner, Christophe GarciaGraph Neural NetworksImage Classification

    INSA Lyon, CNRS, Universit´e Claude Bernard Lyon 1, LIRIS, UMR5205, 69621 Villeurbanne, France

  9. Resolution Allocation with Learnable Resizing and Spectral Filter Parameterization

    Oct 14, 2026Wei Wu, Eric C. LarsonConvolutional Neural NetworksFrequency-Domain Feature Learning

    Department of Computer Science, Southern Methodist University, Dallas, TX, USA

  10. Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine

    Oct 13, 2026Thomas HannaganRecurrent Neural NetworksImage Classification

    Stellantis

  11. Representation Geometry and Inductive Biases in Quantum Classifiers across Classical Visual Benchmarks

    Oct 13, 2026Yu Yamaoka, Weng Ian Chan, Kosuke MitaraiQuantum Machine LearningImage Classification

    Center for Quantum Information and Quantum Biology, The University of Osaka, 1-2 Machikaneyama, Toyonaka, 560-0043, Osaka, Japan. · SANKEN, The University of Osaka, 1-5 Yamadaoka, Suita, 565-0871, Osaka, Japan. · Graduate School of Engineering Science, The University of Osaka, 1-3 Machikaneyama, Toyonaka, 560-8531, Osaka, Japan.

  12. CÆNL: Collapse-Based Active Neural Learning with Objective-Aligned Representation Control

    Oct 13, 2026Saad AlqithamiActive LearningRepresentation Learning

  13. A Geometrical Approach to Constructing Neural Networks for Perfect Training-set Classification

    Oct 12, 2026Akos HajnalNeural Network MemorizationImage Classification

    Laboratory of Parallel and Distributed Systems Institute for Computer Science and Control (SZTAKI) · John von Neumann Faculty of Informatics Obuda University Budapest, Hungary

  14. Detection of extremely rare dead cells in fluorescence microscopy images

    Oct 9, 2026Prateek Verma, Benjamin Butter, Jian Zhang +1Microscopy Image AnalysisFluorescence Microscopy

    Department of Electrical Engineering and Computer Science University of Arkansas, Fayetteville, Arkansas, USA · Department of Biomedical Engineering University of Arkansas, Fayetteville, Arkansas, USA

  15. HAND: A Biologically-Inspired Activation Function that Improves Generalisation and Sample Efficiency in Image Classification

    Oct 8, 2026Michael W. Spratling, Heiko H. SchüttNeural Network Activation FunctionsNeural Network Generalization

    University of Luxembourg, Department of Behavioural and Cognitive Sciences, L-4366 Esch-sur-Alzette, Luxembourg.

  16. MCL: Meta Convolution Layer

    Oct 8, 2026Naim Reza, Md Al Amin, Ho Yub JungDynamic ConvolutionConvolutional Neural Networks

    Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea

  17. Data Leakage in Patch-Based Hyperspectral Image Classification: Quantifying the Impact of Spatial Overlap

    Oct 6, 2026Mohammed Q. AlkhatibData ContaminationHyperspectral Image Classification

    College of Engineering and IT, University of Dubai, Dubai, 14143, UAE

  18. From the Drosophila Visual Connectome to General-Purpose Computer Vision

    Oct 6, 2026Zongyu Li, Akito Yamauchi, Huaizhi Liu +4Graph Neural NetworksImage Classification

    Department of Biomedical Engineering, Columbia University, New York, NY, USA · Department of Electrical Engineering, Columbia University, New York, NY, USA · Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA +2

  19. Test-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration

    Oct 6, 2026Hyeongheon Cha, Young D. Kwon, Sung-Ju LeeTest-Time AdaptationVision Transformer

    KAIST · Samsung AI Center-Cambridge · University of Cambridge

  20. Multi-Dataset Diagnostic Utility of Clinical Visual Concepts in AI Systems for Dermatology

    Oct 6, 2026Linda Wermelinger, Simone Lionetti, Fabian Gröger +6Skin Lesion ClassificationExplainable Medical Image Analysis

    University of Basel · Lucerne University of Applied Sciences and Arts · University Hospital of Basel

  21. Later Is Better: Token Reduction for ViTs Under Distribution Shift

    Oct 6, 2026Hyeongheon Cha, Hyungjun Yoon, Sung-Ju LeeDistribution Shift RobustnessToken Pruning

    KAIST

  22. RPFQ-ViT: Rotated Phase-Frame Quantization for Extremely Low-Bit Weights in Vision Transformers

    Oct 3, 2026Mengyuan Fan, Bokai Huang, JiaMing Pan +4Low-Bit QuantizationRotation-Based Quantization

    Peking University

  23. Specific Algorithmic Interpretability of Neural Networks: A Case Study on Textures

    Oct 3, 2026Yanglin Zhang, Anneke von Seeger, Gilad Lerman +1Wavelet Scattering TransformsNeural Network Interpretability

    Technion – Israel Institute of Technology, Faculty of Mathematics · University of Minnesota, School of Mathematics

  24. You May Be Running the Wrong Inception Crop

    Oct 2, 2026Jason Chuan-Chih ChouData AugmentationImage Classification

    Cohere Labs Community

  25. Selective Backpropagation for Efficient Few-Shot Class-Incremental Learning

    Oct 2, 2026Eeham Khan, Abdulmoumen Al-Atrash, Ali AyubClass-Incremental LearningNeural Network Optimization

    PaInt Lab, Concordia University Montreal, QC, Canada

  26. SigLIP2 for aerial fire risk classification

    Oct 2, 2026Yunus Serhat BıçakçıImage ClassificationWildfire Detection

    Department of Artificial Intelligence and Machine Learning Faculty of Applied Sciences, Marmara University Istanbul, Türkiye

  27. Evolving Hybrid Quantum-Classical Architectures for Image Classification

    Oct 2, 2026Devroop Kar, Daniel Krutz, Travis DesellHybrid Quantum-Classical MLQuantum Architecture Search

    Rochester Institute of Technology Rochester, NY 14623, USA

  28. End-to-End Optical Semantic Communication over a Nonlinear WDM Fiber Link

    Sep 29, 2026Hussein Jammal, Andrea Bianco, Cristina RottondiDeep Joint Source-Channel CodingSemantic Communication

    Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy

  29. Multi-Depth Temporal Fusion for Feedforward, Locally Trained Spiking Neural Networks

    Sep 29, 2026Aidin Attar, Eleonora Cicciarella, Michele RossiSpiking Neural NetworksFeature Fusion

    Department of Information Engineering, University of Padua Padua, Italy

  30. ProtoSeam: Lifting Classifier Training with Latent Gaussian Mixture Models

    Sep 28, 2026Robert Lampel, Timon Klein, Sebastian SagerPrototype-Based ClassificationClassification

    Department of Mathematics, Otto von Guericke University (Magdeburg, Germany) · Max Planck Institute for Dynamics of Complex Technical Systems (Magdeburg, Germany)