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Fine Grained Image Classification On Stanford

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Accuracy
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이 벤치마크에서 각 모델의 성능 결과

Paper Title
TResnet-L + PMD97.3%-Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition
CMAL-Net97.1%-Learn from Each Other to Classify Better: Cross-layer Mutual Attention Learning for Fine-grained Visual Classification
I2-HOFI96.92%-Interweaving Insights: High-Order Feature Interaction for Fine-Grained Visual Recognition
TResNet-L + ML-Decoder96.41%-ML-Decoder: Scalable and Versatile Classification Head
DAT96.2%-Domain Adaptive Transfer Learning with Specialist Models
ALIGN96.13%-Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
SR-GNN96.130.9SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization
EffNet-L2 (SAM)95.96%-Sharpness-Aware Minimization for Efficiently Improving Generalization
SaSPA + CAL95.72-Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation
CAP95.7%-Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification
CSQA-Net95.6%-Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization
AttNet & AffNet95.6%-Fine-Grained Visual Classification with Efficient End-to-end Localization
CCFR95.5%-Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition
CAL95.5%-Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
MPSA95.4%-Multi-Granularity Part Sampling Attention for Fine-Grained Visual Classification
Inceptionv495.35%-Non-binary deep transfer learning for image classification
DCAL95.3%-Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification
API-Net95.3%-Learning Attentive Pairwise Interaction for Fine-Grained Classification
PART95.3%-Part-guided Relational Transformers for Fine-grained Visual Recognition
DenseNet161+MM+FRL95.2%-Learning Class Unique Features in Fine-Grained Visual Classification
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