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 16261650 of 10420 papers

TitleStatusHype
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer ModelsCode1
Can An Image Classifier Suffice For Action Recognition?Code1
Domain Generalization by Learning and Removing Domain-specific FeaturesCode1
NormKD: Normalized Logits for Knowledge DistillationCode1
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification ProblemsCode1
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained EnvironmentsCode1
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift AdaptationCode1
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse TrainingCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Fine-grained Recognition with Learnable Semantic Data AugmentationCode1
CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide ImagesCode1
3D U^2-Net: A 3D Universal U-Net for Multi-Domain Medical Image SegmentationCode1
Fine-grained Classes and How to Find ThemCode1
Circumventing Outliers of AutoAugment with Knowledge DistillationCode1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
Can Biases in ImageNet Models Explain Generalization?Code1
Do text-free diffusion models learn discriminative visual representations?Code1
Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image ClassificationCode1
CHiLS: Zero-Shot Image Classification with Hierarchical Label SetsCode1
An In-depth Study of Stochastic BackpropagationCode1
Can Language Understand Depth?Code1
A Dual-Direction Attention Mixed Feature Network for Facial Expression RecognitionCode1
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?Code1
Conditional Positional Encodings for Vision TransformersCode1
FILIP: Fine-grained Interactive Language-Image Pre-TrainingCode1
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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