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

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Showing 5851–5900 of 10420 papers

TitleStatusHype
Refiner: Refining Self-attention for Vision TransformersCode1
Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution TrainingCode0
ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive BiasCode1
Redundant representations help generalization in wide neural networksCode0
Reveal of Vision Transformers Robustness against Adversarial Attacks—0
Robust Implicit Networks via Non-Euclidean ContractionsCode0
Vision Transformers with Hierarchical AttentionCode1
Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment—0
An End-to-End Breast Tumour Classification Model Using Context-Based Patch Modelling- A BiLSTM Approach for Image Classification—0
FedBABU: Towards Enhanced Representation for Federated Image ClassificationCode1
Efficient Classification of Very Large Images with Tiny ObjectsCode1
Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamicsCode1
RegionViT: Regional-to-Local Attention for Vision TransformersCode1
Meta-Learning with Fewer Tasks through Task InterpolationCode1
GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric CancerCode0
X-volution: On the unification of convolution and self-attention—0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
Nonuniform Defocus Removal for Image Classification—0
Stochastic Whitening Batch Normalization—0
TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data—0
A Comparison for Anti-noise Robustness of Deep Learning Classification Methods on a Tiny Object Image Dataset: from Convolutional Neural Network to Visual Transformer and Performer—0
DynamicViT: Efficient Vision Transformers with Dynamic Token SparsificationCode1
When Vision Transformers Outperform ResNets without Pre-training or Strong Data AugmentationsCode0
SpecRepair: Counter-Example Guided Safety Repair of Deep Neural NetworksCode0
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation—0
Evidential Turing ProcessesCode1
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels—0
Towards Robust Classification Model by Counterfactual and Invariant Data GenerationCode1
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image ClassificationCode1
Container: Context Aggregation NetworkCode1
Learning to Learn Semantic Factors in Heterogeneous Image Classification—0
Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification ModelsCode0
Reconciliation of Statistical and Spatial Sparsity For Robust Image and Image-Set ClassificationCode0
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels—0
Rethinking Pseudo Labels for Semi-Supervised Object Detection—0
Hyperspectral Band Selection for Multispectral Image Classification with Convolutional NetworksCode1
Fidelity Estimation Improves Noisy-Image Classification With Pretrained NetworksCode0
Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling—0
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent—0
Scorpion detection and classification systems based on computer vision and deep learning for health security purposes—0
Dual-stream Network for Visual Recognition—0
Bounded logit attention: Learning to explain image classifiersCode0
Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image RecognitionCode1
MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger TokensCode1
High Performance Hyperspectral Image Classification using Graphics Processing Units—0
TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identificationCode1
EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural NetworkCode1
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
Less is More: Pay Less Attention in Vision TransformersCode1
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified