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 2451–2475 of 10420 papers

TitleStatusHype
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks—0
Just Noticeable Difference for Deep Machine Vision—0
COLORA: Efficient Fine-Tuning for Convolutional Models with a Study Case on Optical Coherence Tomography Image Classification—0
Co-localization with Category-Consistent Features and Geodesic Distance Propagation—0
A Continual Learning Framework for Adaptive Defect Classification and Inspection—0
Collective Learning—0
Collage Inference: Using Coded Redundancy for Low Variance Distributed Image Classification—0
A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning—0
AAVAE: Augmentation-Augmented Variational Autoencoders—0
Collage Inference: Achieving low tail latency during distributed image classification using coded redundancy models—0
Collaborative Image Understanding—0
A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense—0
Do Convolutional Neural Networks Learn Class Hierarchy?—0
Document AI: Benchmarks, Models and Applications—0
Does Distributionally Robust Supervised Learning Give Robust Classifiers?—0
Does Robustness on ImageNet Transfer to Downstream Tasks?—0
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization—0
Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs—0
Adversarial Robustness on Image Classification with k-means—0
CoLa-DCE -- Concept-guided Latent Diffusion Counterfactual Explanations—0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
DM-CT: Consistency Training with Data and Model Perturbation—0
CognitiveNet: Enriching Foundation Models with Emotions and Awareness—0
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning—0
A Push-Pull Layer Improves Robustness of Convolutional Neural Networks—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