Fine-Grained Image Classification
174 papers with code • 35 benchmarks • 36 datasets
Fine-Grained Image Classification is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.
( Image credit: Looking for the Devil in the Details )
Benchmarks
These leaderboards are used to track progress in Fine-Grained Image Classification
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Libraries
Use these libraries to find Fine-Grained Image Classification models and implementationsDatasets
MNIST
CUB-200-2011
Oxford 102 Flower
STL-10
Stanford Cars
Food-101
Caltech-101
iNaturalist
FGVC-Aircraft
EMNIST
Latest papers with no code
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis
The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs.
Semantically-Prompted Language Models Improve Visual Descriptions
With both ideas, we demonstrate that V-GLOSS improves visual descriptions and achieves strong results in the zero-shot setting on general and fine-grained image-classification datasets, including ImageNet, STL-10, FGVC Aircraft, and Flowers 102.
Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification
Fine-grained classification poses greater challenges compared to basic-level image classification due to the visually similar sub-species.
Leaf Cultivar Identification via Prototype-enhanced Learning
Plant leaf identification is crucial for biodiversity protection and conservation and has gradually attracted the attention of academia in recent years.
PVP: Pre-trained Visual Parameter-Efficient Tuning
Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks.
Semantic Feature Integration network for Fine-grained Visual Classification
By eliminating unnecessary features and reconstructing the semantic relations among discriminative features, our SFI-Net has achieved satisfying performance.
An Erudite Fine-Grained Visual Classification Model
Therefore, we first propose a feature disentanglement module and a feature re-fusion module to reduce negative transfer and boost positive transfer between different datasets.
TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification
To this end, two novel modules are proposed to leverage the characteristics of bird images, namely, the hierarchy stage feature aggregation (HSFA) module and the feature in feature abstraction (FFA) module.
Data Augmentation Vision Transformer for Fine-grained Image Classification
Recently, the vision transformer (ViT) has made breakthroughs in image recognition.
Fine-grained Classification of Solder Joints with α-skew Jensen-Shannon Divergence
Detection of solder errors during SJI is quite challenging as the solder joints have very small sizes and can take various shapes.
