SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Showing 19762000 of 10420 papers

TitleStatusHype
Gradient-Guided Annealing for Domain GeneralizationCode1
Gradient Projection Memory for Continual LearningCode1
Graph Convolutional Networks for Hyperspectral Image ClassificationCode1
GradInit: Learning to Initialize Neural Networks for Stable and Efficient TrainingCode1
GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic ForgettingCode1
Can We Talk Models Into Seeing the World Differently?Code1
Graph Attention Transformer Network for Multi-Label Image ClassificationCode1
Confidence-aware multi-modality learning for eye disease screeningCode1
HyperKAN: Kolmogorov-Arnold Networks make Hyperspectral Image Classificators SmarterCode1
Hyperbolic Image-Text RepresentationsCode1
Hyperbolic Image EmbeddingsCode1
The Cascaded Forward Algorithm for Neural Network TrainingCode1
The CLEAR Benchmark: Continual LEArning on Real-World ImageryCode1
GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated LearningCode1
Grid Saliency for Context Explanations of Semantic SegmentationCode1
HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentationCode1
Grounded Situation Recognition with TransformersCode1
Hyperspectral Band Selection for Multispectral Image Classification with Convolutional NetworksCode1
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge DistillationCode1
iDAT: inverse Distillation Adapter-TuningCode1
Group Fisher Pruning for Practical Network CompressionCode1
The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models?Code1
The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network ArchitecturesCode1
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional NetworksCode1
Learning Hierarchical Image Segmentation For Recognition and By RecognitionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified