mlsanityData to 14 Oct 2026

Fine-Grained Image Analysis with Deep Learning: A Survey

arXiv 2111.06119 ↗First posted 11 Nov 2021cs.CV cs.LGcited by 135

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Abstract (arXiv)

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas -- fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community.

Key facts. Cited by 135 papers in the map (data through 14 Oct 2026). Builds on 108 of its 212 references that are in the map. First posted on arXiv 11 Nov 2021.

Builds on

108 of 212 references.

References · 108

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50 most cited of 108 All 108 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 21; 2023: 27; 2024: 31; 2025: 27; 2026: 27.

135 papers.

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  1. 2024INQUIRE: A Natural World Text-to-Image Retrieval Benchmarkcited by 33
  2. 2025Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Modelscited by 31
  3. 2022Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignmentcited by 29
  4. 2023The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understandingcited by 29
  5. 2023Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoningcited by 25
  6. 2024Democratizing Fine-grained Visual Recognition with Large Language Modelscited by 25
  7. 2023Confidence and Dispersity Speak: Characterising Prediction Matrix for Unsupervised Accuracy Estimationcited by 22
  8. 2022SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorizationcited by 21
  9. 2023PDiscoNet: Semantically consistent part discovery for fine-grained recognitioncited by 21
  10. 2025ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generationcited by 21
  11. 2022SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorizationcited by 19
  12. 2024CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scalecited by 18
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  1. 2023Fine-grained Recognition with Learnable Semantic Data Augmentationcited by 17
  2. 2025Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanationcited by 15
  3. 2025Distilling Monocular Foundation Model for Fine-grained Depth Completioncited by 15
  4. 2022On Label Granularity and Object Localizationcited by 12
  5. 2022Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanismcited by 12
  6. 2022Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classificationcited by 12
  7. 2023Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learningcited by 12
  8. 2022SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrievalcited by 11
  9. 2025Taxonomy-Aware Evaluation of Vision-Language Modelscited by 11
  10. 2022An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learningcited by 10
  11. 2024How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?cited by 10
  12. 2022R2-Trans:Fine-Grained Visual Categorization with Redundancy Reductioncited by 8
  13. 2023Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labelscited by 8
  14. 2023Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrievalcited by 8
  15. 2023Equiangular Basis Vectorscited by 7
  16. 2023Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learningcited by 7
  17. 2024Insect Identification in the Wild: The AMI Datasetcited by 7
  18. 2024OccLoff: Learning Optimized Feature Fusion for 3D Occupancy Predictioncited by 7
  19. 2025SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discoverycited by 7
  20. 2021Making a Bird AI Expert Work for You and Mecited by 6
  21. 2023Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognitioncited by 6
  22. 2022Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learningcited by 5
  23. 2023PARTICLE: Part Discovery and Contrastive Learning for Fine-grained Recognitioncited by 5
  24. 2023On Characterizing the Evolution of Embedding Space of Neural Networks using Algebraic Topologycited by 5
  25. 2024Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learningcited by 5
  26. 2024From Attributes to Natural Language: A Survey and Foresight on Text-based Person Re-identificationcited by 5
  27. 2024On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Surveycited by 5
  28. 2024LineArt: A Knowledge-guided Training-free High-quality Appearance Transfer for Design Drawing with Diffusion Modelcited by 5
  29. 2025Same or Not? Enhancing Visual Perception in Vision-Language Modelscited by 5
  30. 2022Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrievalcited by 4
  31. 2023Multi-Granularity Archaeological Dating of Chinese Bronze Dings Based on a Knowledge-Guided Relation Graphcited by 4
  32. 2023Coping with Change: Learning Invariant and Minimum Sufficient Representations for Fine-Grained Visual Categorizationcited by 4
  33. 2023Multi-Scale and Multi-Layer Contrastive Learning for Domain Generalizationcited by 4
  34. 2023DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discoverycited by 4
  35. 2024Data-free Knowledge Distillation for Fine-grained Visual Categorizationcited by 4
  36. 2024Global-Local Similarity for Efficient Fine-Grained Image Recognition with Vision Transformerscited by 4
  37. 2025Learning Part Knowledge to Facilitate Category Understanding for Fine-Grained Generalized Category Discoverycited by 4
  38. 2026Taxonomy-Aware Representation Alignment for Hierarchical Visual Recognition with Large Multimodal Modelscited by 4

50 most cited of 135 All 135 (JSON)