Latest Image Classification Research Papers

The newest Image Classification papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Image Classification so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation
  Vladislav Bargatin, Alexander Yakovenko, Khaled Abud, Dmitriy Vatolin · arXiv · Sep 10, 2026

  Optical flow methods typically rely on task-specific inductive biases, such as correlation volumes, feature warping, and iterative refinement, among others, to reach high accuracy. While effective, such biases constrain the model to predefi…

• Dimensionality Reduction for Hyperspectral Image Classification
  Mohamed Cherifi, Ammar Mesloub, Mohammed Nabil El Korso, Tayeb Touhami et al. · arXiv · Sep 9, 2026

  This paper addresses the issue of supervised classification in the context of hyperspectral satellite images. It deals with two fundamental aspects: dimensionality reduction of data and the selection of appropriate supervised classification…

• Texture Image Classification Using DWT AlexNet Feature Fusion and Deep Neural Networks
  Arun D. Kulkarni · arXiv · Aug 28, 2026

  Texture image classification plays a significant role in computer vision applications, including industrial inspection, medical image analysis, remote sensing, and object recognition. Handcrafted features can capture local texture character…

• Interpretable Fundus Image Classification via Ring-Based Retinal Vasculature Features
  Xiaoyan Li, Shixin Xu, Arvind Gupta, Huaxiong Huang · arXiv · Aug 25, 2026

  Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent representations and provide limited interpretability. This study develops an interpretable …

• TurboT2VA: Fast Large-Scale Text-to-Video-Audio Generation via Score-Regularized Consistency Distillation
  Xiaoda Yang, Yuxiang Liu, Kaiwen Zheng, Yuan Liu et al. · arXiv · Aug 25, 2026

  Joint text-to-video-audio generation produces synchronized visual and acoustic content, but the long sampling trajectories and heterogeneous multimodal computation of large models make inference prohibitively expensive. We present TurboT2VA…

• Counterfactual Contrastive Analysis
  Yunlong He, Pietro Gori · arXiv · Aug 19, 2026

  Visual Counterfactual Explanations (VCEs) aim to explain image classifiers by generating minimally edited and realistic versions of an input image that change the classifier's prediction. Existing VCE methods are inherently classifier-depen…

• Deep Academic Survey: Stateful Agentic Closed-Loop Paradigm for Academic Survey Automation
  Zhikai Xu, Zhucun Xue, Teng Hu, Yabiao Wang et al. · arXiv · Aug 18, 2026

  Academic surveys play a central role in organizing rapidly expanding scholarly literature, yet their construction requires extensive paper analysis, coherent knowledge organization, fine-grained citation support, and reliable manuscript ass…

• DistillPath: An Efficient 22M Distilled Pathology Encoder Approaching Large Foundation Model Performance
  Ramon Kaspar, Andrey Ignatov, Valentina Boeva · arXiv · Aug 18, 2026

  Many high-performing pathology tile encoders are now foundation models with hundreds of millions to over a billion parameters. Encoding and storing the thousands of tiles in each whole-slide image with such models is costly on commodity har…

• MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification
  Daniel Perkins, John Squires, Janou Milligan, Chandra Raskoti et al. · arXiv · Aug 13, 2026

  Modern image classification models excel when trained on single task-specific datasets but often struggle to generalize across domains and difficulty levels. We propose ARMDIL, an Adaptive Router for Multi-Domain Image classification with L…

• Fermat Active Laplace Learning for Semi-Supervised Hyperspectral Image Classification
  Vutichart Buranasiri, James M. Murphy · arXiv · Aug 3, 2026

  Two active learning algorithms for hyperspectral image (HSI) classification are proposed that combine density-aware Fermat distances with Poisson-reweighted harmonic label propagation. Our methods actively query points using an uncertainty-…

• MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification
  Sebastian Doerrich, Daniel Würtinger, Francesco Di Salvo, Shyam Nandan Rai et al. · arXiv · Jul 31, 2026

  Adapting deep learning models to profound clinical heterogeneity typically relies on parameter-efficient fine-tuning (PEFT) to avoid the severe overfitting associated with full end-to-end network updates. Although PEFT successfully navigate…

• Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification
  V. S. Usatyuk, D. A. Sapozhnikov, S. I. Egorov · arXiv · Jul 30, 2026

  We introduce Kohn--Sham Spectral Embedding (KSSE), a physics-inspired energy-based model replacing dense CNN classifiers with a sparse-graph spectral embedding evaluated at the Nishimori temperature of an associated Random-Bond Ising Model.…

• ERUnderstand: Evaluating Vision-Language Models on Structured ER Diagrams
  Ali Ansari, Yasmin Mohammadi, Farnoush Nili, Parsa Esmaeilkhani et al. · arXiv · Jul 27, 2026

  Entity-Relationship Diagrams (ERDs) are central to conceptual database design, yet they are typically available only as rendered images rather than machine-readable schemas, limiting AI-assisted database engineering. We introduce ERUndersta…

• DAPGNet: Dynamic Adaptive Physics-Guided Graph Diffusion Network for Hyperspectral Image Classification
  Pengkun Wang, Weijia Cao, Ning Wang, Xiaofei Yang · arXiv · Jul 16, 2026

  Hyperspectral image (HSI) classification requires reliable pixel-relation modeling under spectral variability, mixed pixels, and heterogeneous boundaries. Existing graph-based HSI classifiers usually construct graph topology from spatial pr…

• Informative Data Reweighting for Image Classification
  Yancheng Wang, Ping Li, Alvin C Silva, Teresa Wu et al. · ICLR 2026 DeLTa Workshop Poster · Mar 3, 2026

  Deep Neural Networks (DNNs) have achieved remarkable success in image classification tasks. However, their training typically requires large-scale, high-quality labeled datasets, which may be scarce or infeasible to obtain in certain comput…

• An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture
  Jingsong Xia, Yue Yin, Xiu Li · arXiv.org · Aug 15, 2025

  Intelligent analysis of medical imaging plays a crucial role in assisting clinical diagnosis. However, achieving efficient and high-accuracy image classification in resource-constrained computational environments remains challenging. This s…

• A review of hyperspectral image classification based on graph neural networks
  Xiaofeng Zhao, Junyi Ma, Lei Wang, Zhili Zhang et al. · Artificial Intelligence Review · Mar 17, 2025

  Hyperspectral images provide rich spectral-spatial information but pose significant classification challenges due to high dimensionality, noise, mixed pixels, and limited labeled samples. Graph Neural Networks (GNNs) have emerged as a promi…

• MedKAN: An Advanced Kolmogorov-Arnold Network for Medical Image Classification
  Zhuoqin Yang, Jiansong Zhang, Xiaoling Luo, Xu Wu et al. · IEEE International Conference on Bioinformatics and Biomedicine · Feb 25, 2025

  Recent advancements in deep learning for image classification predominantly rely on convolutional neural networks (CNNs) or Transformer-based architectures. However, these models face notable challenges in medical imaging, particularly in c…

• Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood Attention
  Omid Nejati Manzari, Hojat Asgariandehkordi, Taha Koleilat, Yiming Xiao et al. · Applied Soft Computing · Feb 19, 2025

  Convolutional networks, transformers, hybrid models, and Mamba-based architectures have demonstrated strong performance across various medical image classification tasks. However, these methods were primarily designed to classify clean imag…

• CNN-Transformer and Channel-Spatial Attention based network for hyperspectral image classification with few samples
  Chuan Fu, Tianyuan Zhou, Tan Guo, Qikui Zhu et al. · Neural Networks · Feb 1, 2025

  Hyperspectral image classification is an important foundational technology in the field of Earth observation and remote sensing. In recent years, deep learning has achieved a series of remarkable achievements in this area. These deep learni…

• Breast cancer histopathology image classification using transformer with discrete wavelet transform.
  Yuting Yan, Ruidong Lu, Jianpeng Sun, Jianxin Zhang et al. · Medical Engineering and Physics · Feb 1, 2025

  Early diagnosis of breast cancer using pathological images is essential to effective treatment. With the development of deep learning techniques, breast cancer histopathology image classification methods based on neural networks develop rap…

• Hyperspectral Image Classification via Cascaded Spatial Cross-Attention Network
  Bo Zhang, Yaxiong Chen, Shengwu Xiong, Xiaoqiang Lu · IEEE Transactions on Image Processing · Jan 29, 2025

  In hyperspectral images (HSIs), different land cover (LC) classes have distinct reflective characteristics at various wavelengths. Therefore, relying on only a few bands to distinguish all LC classes often leads to information loss, resulti…

• SPECIAL: Zero-Shot Hyperspectral Image Classification With CLIP
  Li Pang, Jing Yao, Kaiyu Li, Jun Zhou et al. · IEEE Transactions on Geoscience and Remote Sensing · Jan 27, 2025

  Hyperspectral image (HSI) classification aims to categorize each pixel in an HSI into a specific land cover class, which is crucial for applications such as remote sensing, environmental monitoring, and agriculture. Although deep learning (…

• Vision Transformers for Image Classification: A Comparative Survey
  Yaoli Wang, Yaojun Deng, Yuanjin Zheng, Pratik Chattopadhyay et al. · Technologies · Jan 12, 2025

  Transformers were initially introduced for natural language processing, leveraging the self-attention mechanism. They require minimal inductive biases in their design and can function effectively as set-based architectures. Additionally, tr…

• MambaHSI: Spatial–Spectral Mamba for Hyperspectral Image Classification
  Yapeng Li, Yong Luo, Lefei Zhang, Zengmao Wang et al. · IEEE Transactions on Geoscience and Remote Sensing · Jan 9, 2025

  Transformer has been extensively explored for hyperspectral image (HSI) classification. However, transformer poses challenges in terms of speed and memory usage because of its quadratic computational complexity. Recently, the Mamba model ha…

• Ensemble genetic and CNN model-based image classification by enhancing hyperparameter tuning
  Wajahat Hussain, Muhammad Faheem Mushtaq, Mobeen Shahroz, Urooj Akram et al. · Scientific Reports · Jan 6, 2025

  Model optimization is a problem of great concern and challenge for developing an image classification model. In image classification, selecting the appropriate hyperparameters can substantially boost the model’s ability to learn intricate p…

• EFFResNet-ViT: A Fusion-Based Convolutional and Vision Transformer Model for Explainable Medical Image Classification
  Tahir Hussain, Hayaru Shouno, Abid Hussain, Dostdar Hussain et al. · IEEE Access · Jan 1, 2025

  The rapid advancement of medical imaging technologies requires the development of advanced, automated, and interpretable diagnostic tools for clinical decision-making. Although convolutional neural networks (CNNs) have shown significant pro…

• MCTGCL: Mixed CNN–Transformer for Mars Hyperspectral Image Classification With Graph Contrastive Learning
  Bobo Xi, Yun Zhang, Jiaojiao Li, Tie Zheng et al. · IEEE Transactions on Geoscience and Remote Sensing · Jan 1, 2025

  Hyperspectral image (HSI) classification has been extensively studied in the context of Earth observation. However, its application in Mars exploration remains limited. Although convolutional neural networks (CNNs) have proven effective in …

• Spatial–Spectral Enhancement and Fusion Network for Hyperspectral Image Classification With Few Labeled Samples
  Shuang Liu, C. Fu, Yule Duan, Xiaopan Wang et al. · IEEE Transactions on Geoscience and Remote Sensing · Jan 1, 2025

  Deep learning has shown great potential in hyperspectral image (HSI) classification. However, training these models usually requires a large amount of labeled data. Since the collection of pixel-level annotations for HSIs is laborious and t…

• TransIFC: Invariant Cues-Aware Feature Concentration Learning for Efficient Fine-Grained Bird Image Classification
  Hai Liu, Cheng Zhang, Yongjian Deng, Bochen Xie et al. · IEEE transactions on multimedia · Jan 1, 2025

  Fine-grained bird image classification (FBIC) is not only meaningful for endangered bird observation and protection but also a prevalent task for image classification in multimedia processing and computer vision. However, FBIC suffers from …