Fine-Grained Image Classification
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Benchmarks
Stanford Cars
FGVC Aircraft
CUB-200-2011
NABirds
Oxford 102 Flowers
Stanford Dogs
Oxford-IIIT Pets
Caltech-101
Food-101
Oxford-IIIT Pet Dataset
CUB-200-2011
CompCars
Bird-225
Birdsnap
SUN397
10 Monkey Species
Fruits-360
FoodX-251
Bottles
BoxCars116K
CarFlag-1532
CarFlag-563
Con-Text
DIB-10K
EMNIST-Digits
EMNIST-Letters
FGVC-Aircraft
Herbarium 2022
Imbalanced CUB-200-2011
Kuzushiji-MNIST
MNIST
QMNIST
SOP
STL-10
iNaturalist
Most implemented
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Training data-efficient image transformers & distillation through attention
AutoAugment: Learning Augmentation Policies from Data
DINOv2: Learning Robust Visual Features without Supervision
ResMLP: Feedforward networks for image classification with data-efficient training
Sharpness-Aware Minimization for Efficiently Improving Generalization
Papers
Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classification
Few-shot fine-grained image classification (FSFGIC) aims to classify similar images with limited labeled examples. This work highlights the critical yet underutilized role of phase information in capturing structural rel…
Fine-Grained Image ClassificationPool-Select-Refine for Allocation-Aware Generative Dataset Distillation
Diffusion-based dataset distillation has recently emerged as a promising paradigm for condensing large-scale datasets into compact synthetic sets. By leveraging pretrained generative priors, these methods can produce rea…
Fine-Grained Image ClassificationToolFG: Towards Well-Grounded Fine-Grained Image Classification
Fine-grained image classification (FGIC) has broad applications and has attracted significant research attention. In this paper, we explore a novel paradigm for solving FGIC by proposing \textbf{ToolFG}, the first tool-i…
Fine-Grained Image ClassificationKnowledge DistillationAdaptive receptive field-based spatial-frequency feature reconstruction network for few-shot fine-grained image classification
Feature reconstruction techniques are widely applied for few-shot fine-grained image classification (FSFGIC). Our research indicates that one of the main challenges facing existing feature-based FSFGIC methods is how to …
Fine-Grained Image ClassificationFrequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification
Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples. Existing metric learning-based methods typically rely solely on spa…
Fine-Grained Image ClassificationFine-Grained Visual RecognitionComputational EfficiencyMetric LearningLive Interactive Training for Video Segmentation
Interactive video segmentation often requires many user interventions for robust performance in challenging scenarios (e.g., occlusions, object separations, camouflage, etc.). Yet, even state-of-the-art models like SAM2 …
Fine-Grained Image ClassificationVideo Segmentation