Fine-Grained Visual Recognition

Benchmarks archive 2025-07-28

4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

9 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 43 papers with code (72 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
  26 Apr 2020 8 repositories listed
  In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization…
• Bilinear CNNs for Fine-grained Visual Recognition
  29 Apr 2015 4 repositories listed
  We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also…
• Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
  15 Apr 2019 3 repositories listed Syntology ran 3 of 5 samples · 2 unverified · 3 pointer-only (licence)
  The proposed methods are highly modular, readily plugged into existing deep CNNs.
• Retrieving Similar E-Commerce Images Using Deep Learning
  11 Jan 2019 3 repositories listed
  In this paper, we propose a deep convolutional neural network for learning the embeddings of images in order to capture the notion of visual similarity.
• RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition
  20 Mar 2024 2 repositories listed Syntology ran 5 of 7 samples · 2 unverified
  Notably, our approach demonstrates a significant improvement in performance on 5 fine-grained visual recognition benchmarks, 11 few-shot image recognition datasets, and the 2 object detection datasets under the…
• CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification
  29 Apr 2022 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation.
• Feathers dataset for Fine-Grained Visual Categorization
  18 Apr 2020 2 repositories listed
  This paper introduces a novel dataset FeatherV1, containing 28, 272 images of feathers categorized by 595 bird species.
• X-Linear Attention Networks for Image Captioning
  31 Mar 2020 2 repositories listed
  Recent progress on fine-grained visual recognition and visual question answering has featured Bilinear Pooling, which effectively models the 2ⁿᵈ order interactions across multi-modal inputs.
• MILDNet: A Lightweight Single Scaled Deep Ranking Architecture
  3 Mar 2019 2 repositories listed
  Inspired by the fact that successive CNN layers represent the image with increasing levels of abstraction, we compressed our deep ranking model to a single CNN by coupling activations from multiple intermediate layers…
• Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition
  26 Jul 2018 2 repositories listed
  Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning.
• Metric Learning with Adaptive Density Discrimination
  18 Nov 2015 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Beyond classification, we further validate the saliency of the learnt representations via their attribute concentration and hierarchy recovery properties, achieving 10-25% relative gains on the softmax classifier and…
• Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data
  19 Feb 2025 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 1 pointer-only (licence)
  Large multimodal models (LMMs) have shown impressive capabilities in a wide range of visual tasks.
• Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models
  25 Jan 2025 1 repository listed Syntology ran 4 of 6 samples · 2 unverified · 6 pointer-only (licence)
  Multi-modal large language models (MLLMs) have shown remarkable abilities in various visual understanding tasks.
• Interweaving Insights: High-Order Feature Interaction for Fine-Grained Visual Recognition
  20 Oct 2024 1 repository listed
  This paper presents a novel approach for Fine-Grained Visual Classification (FGVC) by exploring Graph Neural Networks (GNNs) to facilitate high-order feature interactions, with a specific focus on constructing both…
• Enhancing Fine-Grained Visual Recognition in the Low-Data Regime Through Feature Magnitude Regularization
  3 Sep 2024 1 repository listed
  Moreover, in the context of building FGVR models with limited data, these irrelevant features can dominate the training process, overshadowing more useful, generalizable discriminative features.
• Towards Generative Class Prompt Learning for Fine-grained Visual Recognition
  3 Sep 2024 1 repository listed
  Specifically, we propose two novel methods: Generative Class Prompt Learning (GCPL) and Contrastive Multi-class Prompt Learning (CoMPLe).
• Extremely Fine-Grained Visual Classification over Resembling Glyphs in the Wild
  25 Aug 2024 1 repository listed
  In the second stage, we introduce CCFG-Net, a network architecture that integrates classification and contrastive learning in both Euclidean and Angular spaces, in which contrastive learning is applied in both…
• Extract More from Less: Efficient Fine-Grained Visual Recognition in Low-Data Regimes
  28 Jun 2024 1 repository listed
  The emerging task of fine-grained image classification in low-data regimes assumes the presence of low inter-class variance and large intra-class variation along with a highly limited amount of training samples per…
• Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)
  3 Jun 2024 1 repository listed
  Understanding Activities of Daily Living (ADLs) is a crucial step for different applications including assistive robots, smart homes, and healthcare.
• HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding
  23 Nov 2023 1 repository listed Syntology ran 3 of 6 samples · 3 unverified
  We explore constructing the class hierarchy into a graph, with its nodes representing the textual or image features of each category.
• Dynamic Conceptional Contrastive Learning for Generalized Category Discovery
  30 Mar 2023 1 repository listed
  This leads traditional novel category discovery (NCD) methods to be incapacitated for GCD, due to their assumption of unlabeled data are only from novel categories.
• Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems
  3 Mar 2023 1 repository listed
  Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns.
• Fine-Grained Visual Classification via Internal Ensemble Learning Transformer
  13 Feb 2023 1 repository listed
  The proposed IELT involves three main modules: multi-head voting (MHV) module, cross-layer refinement (CLR) module, and dynamic selection (DS) module.
• Multi-View Active Fine-Grained Visual Recognition
  1 Jan 2023 1 repository listed
  Despite the remarkable progress of Fine-grained visual classification (FGVC) with years of history, it is still limited to recognizing 2 images.
• Part-guided Relational Transformers for Fine-grained Visual Recognition
  28 Dec 2022 1 repository listed
  This framework, namely PArt-guided Relational Transformers (PART), is proposed to learn the discriminative part features with an automatic part discovery module, and to explore the intrinsic correlations with a feature…
• Penalizing the Hard Example But Not Too Much: A Strong Baseline for Fine-Grained Visual Classification
  21 Nov 2022 1 repository listed
  Second, we instantiate the loss function and provide a strong baseline for FGVC, where the performance of a naive backbone can be boosted and be comparable with recent methods.
• Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism
  1 Aug 2022 1 repository listed
  In low data regimes, a network often struggles to choose the correct regions for recognition and tends to overfit spurious correlated patterns from the training data.
• MemSAC: Memory Augmented Sample Consistency for Large Scale Unsupervised Domain Adaptation
  25 Jul 2022 1 repository listed Syntology ran 2 of 3 samples · 1 unverified
  Practical real world datasets with plentiful categories introduce new challenges for unsupervised domain adaptation like small inter-class discriminability, that existing approaches relying on domain invariance alone…
• On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition
  26 May 2022 1 repository listed Syntology ran 3 of 17 samples · 14 unverified
  Inspired by this observation, we propose a network branch dedicated to magnifying the importance of small eigenvalues.
• Do Vision-Language Pretrained Models Learn Composable Primitive Concepts?
  31 Mar 2022 1 repository listed Syntology ran 4 of 4 samples · 0 unverified
  CompMap first asks a VL model to generate primitive concept activations with text prompts, and then learns to construct a composition model that maps the primitive concept activations (e.

Syntology lines on 10 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.