Fine-Grained Image Classification

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2 papers in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

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A Large-Scale Study on the Accuracy vs Cost Trade-offs of Training and Evaluation Settings in Fine-Grained Image Recognition
May 18, 2026Edwin Arkel Rios, Augusto Christian Surya, Oswin Gosal +5Data Augmentation Cost-Aware Inference
National Yang Ming Chiao Tung University, Taiwan · National Tsing Hua University, Taiwan
How to Choose Your Teacher for Fine Grained Image Recognition
May 15, 2026Oswin Gosal, Edwin Arkel Rios, Augusto Christian Surya +3Fine-Grained Image Classification Teacher-Student Learning
National Tsing Hua University, Taiwan · National Yang Ming Chiao Tung University, Taiwan
Pretraining Objective Matters in Extreme Low-Data FGVC: A Backbone-Controlled Study
May 15, 2026Alexander Hackett, Srikanth Thudumu, Ginny Fisher +1Contrastive Learning Masked Autoencoders
Santa Clara University · IAAIR
When Labels Have Structure: Improving Image Classification with Hierarchy-Aware Cross-Entropy
May 7, 2026April Chan, Davide D'Ascenzo, Sebastiano Cultrera di MontesanoHierarchical Classification Fine-Grained Image Classification
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA · Department of Computer Science, University of Milan, Milan, Italy · Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy +1
Extremely coarse learning objectives induce human-aligned representations in AI vision models
May 7, 2026Yash Mehta, Michael F. BonnerVisual Representation Learning Fine-Grained Image Classification
Department of Cognitive Science, Johns Hopkins University
FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis
May 6, 2026Woojin Lee, Pranav Mekkoth, Ye Tian +2Hierarchical Classification Fine-Grained Image Classification
Department of Computer Science and Engineering University of California, San Diego (UCSD)
Leveraging Vision-Language Models as Weak Annotators in Active Learning
May 1, 2026Phuong Ngoc Nguyen, Kaito Shiku, Ryoma Bise +2Vision-Language Models Active Learning
Kyushu University, Japan.
Self-Supervised Learning of Plant Image Representations
Apr 30, 2026Ilyass Moummad, Kawtar Zaher, Hervé Goëau +3Data Augmentation Fine-Grained Image Classification
INRIA, LIRMM, Université de Montpellier, Montpellier, France · Institut National de l’Audiovisuel, Paris, France · AMAP, Université de Montpellier, CIRAD, INRAE, IRD, Montpellier, France
Geometry-Guided Self-Supervision for Ultra-Fine-Grained Recognition with Limited Data
Apr 21, 2026Shijie Wang, Yadan Luo, Zijian Wang +3Fine-Grained Image Classification Geometric Representation Learning
College of Computer Science and Engineering, Shandong University of Science and Technology, China · School of Information Technology and Electrical Engineering, The University of Queensland, Australia
T-DuMpRa: Teacher-guided Dual-path Multi-prototype Retrieval Augmented framework for fine-grained medical image classification
Apr 19, 2026Zixuan Tang, Shen ZhaoPrototype-Based Classification Retrieval-Augmented Classification
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China
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
FD $^2$ : A Dedicated Framework for Fine-Grained Dataset Distillation
Mar 26, 2026Hongxu Ma, Guang Li, Shijie Wang +5Fine-Grained Image Classification Dataset Distillation
Zhejiang University · Hokkaido University · The University of Queensland +2
Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification
Sep 29, 2025Yinghao Jin, Xi YangActive Learning Fine-Grained Image Classification
Jilin University, China
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

May 18, 2026, cs.CVJ/K move · Enter open · S save

A Large-Scale Study on the Accuracy vs Cost Trade-offs of Training and Evaluation Settings in Fine-Grained Image Recognition

Edwin Arkel Rios, Augusto Christian Surya, Oswin Gosal, Fernando Mikael+4

National Yang Ming Chiao Tung University, Taiwan · National Tsing Hua University, Taiwan

Data Augmentation Cost-Aware Inference Fine-Grained Image Classification Fine-Grained Visual Perception

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Prior work on fine-grained image recognition (FGIR) has established the importance of the backbone selection, but has neglected the accuracy-vs-cost trade-offs under different training and evaluation settings. In this work we conduct a large-scale study with over 2000 experiments across 6 training and evaluation settings, 9 pretrained backbones, and 17 datasets. Preliminary observations on the effectiveness of data augmentation for fine-grained training motivate us to extend Counterfactual Attention Learning (CAL), a state-of-the-art method based on data-aware cropping and masking augmentations, with cross-image discriminative region mixing augmentation. We also propose an efficient evaluation-only variant that maintains competitive accuracy while reducing inference costs by forfeiting the forward pass on discriminative crops that is normally used by CAL and similar FGIR methods. Our results show that data-aware augmentations during training only can enable a model to achieve excellent accuracy even without crops, significantly reducing inference costs. To support future research we share our code and checkpoints at: https://github.com/arkel23/FGIR-Backbones

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