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

• 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…

• Learning to Unify Deformable Shape and Texture Representations for Cardiac Video Classification
  Tonmoy Hossain, Miaomiao Zhang · arXiv · Jul 8, 2026

  Deformable shape representations have proven to be robust complements to texture features in cardiac image classification, offering geometric priors that are invariant to imaging artifacts and intensity variations. However, existing deep ne…

• Learning from Reliable Latent Prompts for Visual Recognition with Missing Modalities
  Taixi Chen, Nancy Guo · arXiv · Jun 29, 2026

  Large-scale multimodal models (LMMs) have achieved superior performance in visual recognition by synergizing information across diverse, massive-scale paired modalities. In real-world scenarios, however, missing-modality inputs are ubiquito…

• FR-DETR: Frequency and Recurrent Feature Refinement for Robust Object Detection under Adverse Weather
  Tuan-Duc Nguyen, Duc-Trong Le · arXiv · Jun 29, 2026

  Object detection under adverse weather remains challenging due to severe visual degradations and domain shifts. Existing enhancer-based approaches attempt to improve detection by cascading an enhancer with a detector, but they introduce red…

• FunPiQ: A New Benchmark for Pixel-Level Quality Assessment in Fundus Images
  Pengwei Wang, José Morano, Virginia Mares, Hrvoje Bogunović · arXiv · Jun 24, 2026

  Color fundus photography (CFP) is the most common ophthalmic imaging modality for large-scale screening. However, it is highly susceptible to degradations, making robust fundus image quality assessment (FIQA) crucial. The criteria for what …

• Adaptive Hebbian Memory Routing in Vision Transformers for Few-Shot Learning
  Mohammed Yusuf Mujawar, Noorbakhsh Amiri Golilarz · arXiv · Jun 23, 2026

  Few-shot image recognition requires models to adapt to new classes from a small labeled support set. Hebbian fast-weight memory can provide temporary associative information during an episode, but fixed memory behavior may not be appropriat…

• Hedgementation = Hedgerow Segmentation: A Remote Sensing Benchmark
  Nathan Senyard, Salem Hamdani, Astrid Zhang, Derek Wang et al. · arXiv · Jun 22, 2026

  We propose Hedgementation: a new benchmark to evaluate machine learning models for hedgerow mapping from remote sensing data at country scale and 10m$^2$ spatial resolution. We combine and harmonize multiple remote sensing data products and…

• When LLMs Analyze Scars: From Images to Clinically-Meaningful Features
  Ruman Wang, Hangting Ye · arXiv · Jun 16, 2026

  Medical image classification faces a fundamental dilemma: while deep learning models achieve remarkable performance at scale, real-world clinical scenarios often suffer from severe data scarcity due to annotation costs, privacy constraints,…

• The Importance of Phase in Neural Representations: An Internal Oppenheim-Lim Test of Image Classifiers
  Alper Yıldırım · arXiv · Jun 15, 2026

  Oppenheim and Lim (1981) showed that natural images stay recognizable when reconstructed from their Fourier phase alone, while the magnitude carries little of their identity. We ask whether trained image classifiers reproduce this asymmetry…

• Rethinking Global Average Pooling: Your Classifier Is Secretly a Multi-Instance Learner
  Aray Karjauv · arXiv · Jun 12, 2026

  Modern image classifiers widely adopt global average pooling (GAP) followed by a linear classification head. This linearity ensures that the image-level logits equal the average of logits obtained by applying the classification head pointwi…

• What's Old is New Again: Classical Dimensionality Reduction for Efficient Saliency-Guided Biometric Attack Detection
  Samuel Webster, Walter Scheirer · arXiv · Jun 11, 2026

  Saliency-guided training is a paradigm in visual recognition that encourages models to focus on the most relevant image regions during learning. While its application in biometric presentation attack detection (PAD) has shown strong benefit…

• Adversarial Attack and Disturbance Detection by Hadamard-Coded Output Representations for Object Detection and Semantic Segmentation
  Lucas Görnhardt, Timo Bartels, Niklas Schwarz, Tim Fingscheidt · arXiv · Jun 8, 2026

  Conventional one-hot encodings often yield poorly calibrated models, being overconfident under attack, and letting entropy-based detection algorithms fail. Previous image classification works have demonstrated that Hadamard-coded output rep…

• ToolFG: Towards Well-Grounded Fine-Grained Image Classification
  Yu Xue, Haoxuan Qu, Zhuoling Li, Yihang Lou et al. · arXiv · Jun 1, 2026

  Fine-grained image classification (FGIC) has broad applications and has attracted significant research attention. In this paper, we explore a novel paradigm for solving FGIC by proposing \textbf{ToolFG}, the first tool-integrated MLLM-based…

• BiasEdit: A Training-Free Bias-Detect-and-Edit Framework for Learning Fair Visual Classifiers
  Jungwook Seo, Yoonsik Park, Changmin Lee, Sungyong Baik · arXiv · May 27, 2026

  Visual data from the Web power image classifiers, which often underpin many web services, such as recommendation and content moderation. However, the raw Web data often contain spurious correlations and social biases, and neural networks ar…

• 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…

• 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…

• Improving pneumonia diagnosis with high-accuracy CNN-Based chest X-ray image classification and integrated gradient
  Jalal Rabbah, Mohammed Ridouani, L. Hassouni · Biomedical Signal Processing and Control · Mar 1, 2025
• AI-Powered Lung Cancer Detection: Assessing VGG16 and CNN Architectures for CT Scan Image Classification
  Rapeepat Klangbunrueang, Pongsathon Pookduang, Wirapong Chansanam, Tassanee Lunrasri · Informatics · Feb 11, 2025

  Lung cancer is a leading cause of mortality worldwide, and early detection is crucial in improving treatment outcomes and reducing death rates. However, diagnosing medical images, such as Computed Tomography scans (CT scans), is complex and…

• Emerging Developments in Real-Time Edge AIoT for Agricultural Image Classification
  M. Pintus, Felice Colucci, Fabio Maggio · IoT · Feb 10, 2025

  Advances in deep learning (DL) models and next-generation edge devices enable real-time image classification, driving a transition from the traditional, purely cloud-centric IoT approach to edge-based AIoT, with cloud resources reserved for…

• 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…

• 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…

• 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, Xiangyong Cao · arXiv.org · 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-b…

• 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…

• 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…

• 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…

• 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 …