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 7826–7850 of 10420 papers

TitleStatusHype
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension—0
Robust Contrastive Active Learning with Feature-guided Query Strategies—0
Deep Dictionary Learning: A PARametric NETwork Approach—0
RecNets: Channel-wise Recurrent Convolutional Neural Networks—0
Recognizing Image Objects by Relational Analysis Using Heterogeneous Superpixels and Deep Convolutional Features—0
Recognizing Images with at most one Spike per Neuron—0
Improving Resnet-9 Generalization Trained on Small Datasets—0
Deep Dependency Networks for Multi-Label Classification—0
Improving Quaternion Neural Networks with Quaternionic Activation Functions—0
Deep Degradation Prior for Low-Quality Image Classification—0
Deep Decision Network for Multi-Class Image Classification—0
Reconstruction Regularized Deep Metric Learning for Multi-label Image Classification—0
Auxiliary Class Based Multiple Choice Learning—0
Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception—0
Improving plant disease classification by adaptive minimal ensembling—0
Recovering Localized Adversarial Attacks—0
Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis—0
Improving Performance of Semi-Supervised Learning by Adversarial Attacks—0
Deep Curriculum Learning for PolSAR Image Classification—0
Recurrent Attention Unit—0
Improving Object Detection with Selective Self-supervised Self-training—0
Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning—0
Improving Normalization with the James-Stein Estimator—0
Recurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classification—0
Auto-view contrastive learning for few-shot image recognition—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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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