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

• VPR-Space: A Benchmark for Visual Place Recognition in Space Analog Domains
  Richard Fechner, Peer Neubert · arXiv · Oct 26, 2026

  Visual Place Recognition (VPR) is an important means for loop closure detection in visual Simultaneous Localization and Mapping (SLAM). While state-of-the-art VPR and SLAM approaches excel in urban and feature-rich natural environments, the…

• STROKE: Simple Sparse Structural Occlusions for Black-Box Adversarial Attacks
  Ankit Gupta, Akshat Pandya, Paul Lin, Christoph Adami et al. · arXiv · Oct 23, 2026

  We introduce STROKE, a query-efficient score-based black-box adversarial attack that constructs perturbations from simple oriented rectangular occlusions. STROKE optimizes these perturbations through a lightweight greedy add-and-refine proc…

• Stochastic Nonlinearities Improve Uncertainty Estimation
  Mohammad Sarhangzadeh, Amirreza M. Shebly, Doruk Oner · arXiv · Oct 21, 2026

  Reliable uncertainty estimation is important across visual recognition tasks, providing information about the confidence and reliability of model predictions. Many uncertainty estimation methods obtain predictive diversity through mechanism…

• Compositionality with Primitives: A Unified View from Euclidean to Hyperbolic Vision-Language Models
  Puru Vaish, Meelad Dashti, Christoph Brune, Pascal Mettes et al. · arXiv · Oct 21, 2026

  Compositional zero-shot learning (CZSL) aims to recognize novel combinations of previously observed objects and attributes. Prior work showed that large-scale vision-language models encode decomposable semantic structures that can be recomp…

• Copyright Leakage Detection in AI Generated Videos
  Shaif Chowdhury, Moshiur Rahman Tonmoy · arXiv · Oct 20, 2026

  Video generation models can create realistic-looking video footage from images and text prompts. Some of the recent advances have raised concerns about the reproduction of copyrighted visual content. However, copyright leakage detection in …

• Data Leakage in Patch-Based Hyperspectral Image Classification: Quantifying the Impact of Spatial Overlap
  Mohammed Q. Alkhatib · arXiv · Oct 6, 2026

  Patch-based learning improves hyperspectral image (HSI) classification by exploiting local spectral-spatial information, but random train-test sampling from the same image can cause spatial patch overlap, leading to data leakage and optimis…

• Image Classifiers are Efficient Self-Supervised Video Representation Learners
  Owais Iqbal, Sudipta Sarkar, Shyam Marjit, Omprakash Chakraborty et al. · arXiv · Sep 30, 2026

  We introduce VideoMSN, a Masked Siamese Network framework for efficient self-supervised spatio-temporal representation learning in videos. Instead of relying on heavy 3D architectures or reconstruction-based autoencoders for learning with u…

• Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification
  Ilán Carretero, Pablo Meseguer, Rocío del Amor, Valery Naranjo · arXiv · Sep 23, 2026

  Pathology foundation models (PFMs) have transformed computational pathology through powerful representation learning from histopathological images. PFMs provide rich, discriminative representations for whole slide image (WSI) analysis, enab…

• Label-Efficient Learning for Ground-Based Sky-Image Classification: A Benchmark of Transfer Learning, Active Learning, and Pseudo-Labeling on GCD
  Esther Bou Dagher, Viktoriya Bu-Dager, Boguslaw Zegarlinski · arXiv · Sep 22, 2026

  Accurate ground-based cloud classification is important for atmospheric monitoring, solar-energy forecasting, aviation weather assessment, and climate observation systems. However, reliable sky-image annotation is time-consuming, especially…

• Informative Data Reweighting for Image Classification
  ICLR 2027 Conference Submission · Sep 18, 2026

  The success of Deep Neural Networks (DNNs) in image classification largely depends on access to large-scale, high-quality labeled datasets, which are often scarce or costly to obtain. To alleviate this challenge, Generative Data Augmentatio…

• Balanced Prompt Adaptation against Entropy-Induced Collapse for Test-Time Binary Segmentation
  Zhengshan Wang, Joshua Charles Webster-Ford, Yifei Tian, Xinxin Wang et al. · arXiv · Sep 18, 2026

  Entropy minimization is a standard objective for test-time adaptation (TTA), but it can fail in imbalanced binary segmentation. Unlike image classification, dense segmentation aggregates thousands of pixel predictions, allowing the larger p…

• 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…

• 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, Zhi-Li 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
  Zhuo-Qin Yang, Jiansong Zhang, Xiao-Ling 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…

• Breast cancer histopathology image classification using transformer with discrete wavelet transform.
  Yuting Yan, Ruidong Lu, Jian Sun, Jian-Xin 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, Tian-Yuan Zhou, Tan Guo, Qi-Kui 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, Ya-Xiong Chen, Sheng-Wu Xiong, Xiao-Qiang 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, Kai-Yu 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, Yuan-Jin 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
  Ya-Peng Li, Yong Luo, Le-Fei Zhang, Zeng-Mao 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, M. F. 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…

• TransIFC: Invariant Cues-Aware Feature Concentration Learning for Efficient Fine-Grained Bird Image Classification
  Hai Liu, Cheng Zhang, Yongjian Deng, Bo-Chen 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 …

• MCTGCL: Mixed CNN–Transformer for Mars Hyperspectral Image Classification With Graph Contrastive Learning
  Bobo Xi, Yun Zhang, Jiao-Jiao 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, Chuan Fu, Yu-Le 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…

• Noise-Augmented Deep Neural Networks for Image Classification: Insights from Information Theory
  Xiaowei Yu, Zhe Huang, Yao Xue, Dajiang Zhu · Submitted to ICLR 2025 · Sep 25, 2024

  In this study, we explore the impact of proactively injecting noise into deep learning models, focusing particularly on classification problems, such as image classification and domain adaptation. While noise is typically seen as harmful, o…