Fine-Grained Image Classification
172 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
Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning
We show here that the combination of a large language model and an image generation model can similarly provide useful premonitions as to how a continual learning challenge might develop over time.
Invariant Test-Time Adaptation for Vision-Language Model Generalization
mahuanaaa/intta
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Vision-language foundation models have exhibited remarkable success across a multitude of downstream tasks due to their scalability on extensive image-text paired datasets.
Improved Zero-Shot Classification by Adapting VLMs with Text Descriptions
By prompting LLMs in various ways, we generate descriptions that capture visual appearance, habitat, and geographic regions and pair them with existing attributes such as the taxonomic structure of the categories.
Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning
While semi-supervised learning (SSL) has yielded promising results, the more realistic SSL scenario remains to be explored, in which the unlabeled data exhibits extremely high recognition difficulty, e. g., fine-grained visual classification in the context of SSL (SS-FGVC).
Good Questions Help Zero-Shot Image Reasoning
QVix enables a wider exploration of visual scenes, improving the LVLMs' reasoning accuracy and depth in tasks such as visual question answering and visual entailment.
GIFT: Generative Interpretable Fine-Tuning Transformers
For the latter, in contrast to the prior art that directly introduce new model parameters (often in low-rank approximation form) to be learned in fine-tuning with downstream data, we propose a method for learning to generate the fine-tuning parameters.
Meta Co-Training: Two Views are Better than One
jayrothenberger/meta-co-training
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We show that in the common case when independent views are not available we can construct such views inexpensively using pre-trained models.
A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis
imageomics/intr
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Unlike mainstream classifiers that wait until the last fully-connected layer to incorporate class information to make predictions, we investigate a proactive approach, asking each class to search for itself in an image.
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping
mvrl/birdsat
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We propose a metadata-aware self-supervised learning~(SSL)~framework useful for fine-grained classification and ecological mapping of bird species around the world.
Gramian Attention Heads are Strong yet Efficient Vision Learners
lab-lvm/imagenet-models
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ICCV 2023
We introduce a novel architecture design that enhances expressiveness by incorporating multiple head classifiers (\ie, classification heads) instead of relying on channel expansion or additional building blocks.
