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SOTA
少样本图像分类
Few Shot Image Classification On Mini 2
Few Shot Image Classification On Mini 2
评估指标
Accuracy
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Accuracy
Paper Title
SgVA-CLIP
97.95
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification
CAML [Laion-2b]
96.2
Context-Aware Meta-Learning
P>M>F (P=DINO-ViT-base, M=ProtoNet)
95.3
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
TRIDENT
86.11
Transductive Decoupled Variational Inference for Few-Shot Classification
PT+MAP+SF+SOT (transductive)
85.59
The Self-Optimal-Transport Feature Transform
PT+MAP+SF+BPA (transductive)
85.59
The Balanced-Pairwise-Affinities Feature Transform
PEMnE-BMS* (transductive)
85.54
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
PT+MAP (s+f) (transductive)
84.81
Few-Shot Learning by Integrating Spatial and Frequency Representation
BAVARDAGE
84.80
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
EASY 3xResNet12 (transductive)
84.04
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
Illumination Augmentation
82.99
Sill-Net: Feature Augmentation with Separated Illumination Representation
PT+MAP (transductive)
82.92
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
EASY 2xResNet12 1/√2 (transductive)
82.31
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
Transductive CNAPS + FETI
79.9
Enhancing Few-Shot Image Classification with Unlabelled Examples
PrototypeCompletion
79.01
Prototype Completion for Few-Shot Learning
SemFew-Trans
78.94
Simple Semantic-Aided Few-Shot Learning
MCT
78.55
Meta-Learned Confidence for Few-shot Learning
TIM-GD
77.80
Transductive Information Maximization For Few-Shot Learning
Simple CNAPS + FETI
77.4
Improved Few-Shot Visual Classification
EPNet
77.27
Embedding Propagation: Smoother Manifold for Few-Shot Classification
0 of 105 row(s) selected.
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HyperAI
HyperAI超神经
首页
算力平台
文档
资讯
论文
教程
数据集
百科
SOTA
LLM 模型天梯
GPU 天梯
顶会
开源项目
全站搜索
关于
服务条款
隐私政策
中文
HyperAI
HyperAI超神经
Toggle Sidebar
全站搜索…
⌘
K
Command Palette
Search for a command to run...
算力平台
首页
SOTA
少样本图像分类
Few Shot Image Classification On Mini 2
Few Shot Image Classification On Mini 2
评估指标
Accuracy
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Accuracy
Paper Title
SgVA-CLIP
97.95
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification
CAML [Laion-2b]
96.2
Context-Aware Meta-Learning
P>M>F (P=DINO-ViT-base, M=ProtoNet)
95.3
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
TRIDENT
86.11
Transductive Decoupled Variational Inference for Few-Shot Classification
PT+MAP+SF+SOT (transductive)
85.59
The Self-Optimal-Transport Feature Transform
PT+MAP+SF+BPA (transductive)
85.59
The Balanced-Pairwise-Affinities Feature Transform
PEMnE-BMS* (transductive)
85.54
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
PT+MAP (s+f) (transductive)
84.81
Few-Shot Learning by Integrating Spatial and Frequency Representation
BAVARDAGE
84.80
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
EASY 3xResNet12 (transductive)
84.04
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
Illumination Augmentation
82.99
Sill-Net: Feature Augmentation with Separated Illumination Representation
PT+MAP (transductive)
82.92
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
EASY 2xResNet12 1/√2 (transductive)
82.31
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
Transductive CNAPS + FETI
79.9
Enhancing Few-Shot Image Classification with Unlabelled Examples
PrototypeCompletion
79.01
Prototype Completion for Few-Shot Learning
SemFew-Trans
78.94
Simple Semantic-Aided Few-Shot Learning
MCT
78.55
Meta-Learned Confidence for Few-shot Learning
TIM-GD
77.80
Transductive Information Maximization For Few-Shot Learning
Simple CNAPS + FETI
77.4
Improved Few-Shot Visual Classification
EPNet
77.27
Embedding Propagation: Smoother Manifold for Few-Shot Classification
0 of 105 row(s) selected.
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