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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4776–4800 of 10420 papers

TitleStatusHype
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World—0
Augmenting learning using symmetry in a biologically-inspired domain—0
Improving training of deep neural networks via Singular Value Bounding—0
Augmenting Image Question Answering Dataset by Exploiting Image Captions—0
Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance—0
A large-scale field test on word-image classification in large historical document collections using a traditional and two deep-learning methods—0
Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Models—0
Improving Transferability of Deep Neural Networks—0
DAF-NET: a saliency based weakly supervised method of dual attention fusion for fine-grained image classification—0
DAFD: Domain Adaptation via Feature Disentanglement for Image Classification—0
Adaptable image quality assessment using meta-reinforcement learning of task amenability—0
Improving the Effectiveness of Deep Generative Data—0
D^2: Decentralized Training over Decentralized Data—0
A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes—0
D^2: Decentralized Training over Decentralized Data—0
Human-interpretable model explainability on high-dimensional data—0
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction—0
Accelerating Neural Network Inference by Overflow Aware Quantization—0
Improving the Reliability for Confidence Estimation—0
Augmented Conditioning Is Enough For Effective Training Image Generation—0
Cyclic orthogonal convolutions for long-range integration of features—0
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism—0
Human Imperceptible Attacks and Applications to Improve Fairness—0
Human Face Recognition using Gabor based Kernel Entropy Component Analysis—0
Improving Tail-Class Representation with Centroid Contrastive Learning—0
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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