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 15011525 of 10420 papers

TitleStatusHype
Vision Transformers with Hierarchical AttentionCode1
Meta-Learning with Fewer Tasks through Task InterpolationCode1
Efficient Classification of Very Large Images with Tiny ObjectsCode1
RegionViT: Regional-to-Local Attention for Vision TransformersCode1
FedBABU: Towards Enhanced Representation for Federated Image ClassificationCode1
Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamicsCode1
DynamicViT: Efficient Vision Transformers with Dynamic Token SparsificationCode1
Evidential Turing ProcessesCode1
Towards Robust Classification Model by Counterfactual and Invariant Data GenerationCode1
Container: Context Aggregation NetworkCode1
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image ClassificationCode1
Hyperspectral Band Selection for Multispectral Image Classification with Convolutional NetworksCode1
MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger TokensCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image RecognitionCode1
EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural NetworkCode1
TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identificationCode1
Less is More: Pay Less Attention in Vision TransformersCode1
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image ClassificationCode1
ResT: An Efficient Transformer for Visual RecognitionCode1
Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by DesignCode1
Predict then Interpolate: A Simple Algorithm to Learn Stable ClassifiersCode1
Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual UnderstandingCode1
FedScale: Benchmarking Model and System Performance of Federated Learning at ScaleCode1
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the FlyCode1
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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