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 6301–6325 of 10420 papers

TitleStatusHype
Tessellated 2D Convolution Networks: A Robust Defence against Adversarial Attacks—0
Less is More: Dimension Reduction Finds On-Manifold Adversarial Examples in Hard-Label Attacks—0
A Class of Short-term Recurrence Anderson Mixing Methods and Their Applications—0
UNCERTAINTY QUANTIFICATION USING VARIATIONAL INFERENCE FOR BIOMEDICAL IMAGE SEGMENTATION—0
Learning to Schedule Learning rate with Graph Neural Networks—0
To Smooth or not to Smooth? On Compatibility between Label Smoothing and Knowledge Distillation—0
Towards Generic Interface for Human-Neural Network Knowledge Exchange—0
Two Birds, One Stone: Achieving both Differential Privacy and Certified Robustness for Pre-trained Classifiers via Input Perturbation—0
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification—0
Best Practices in Pool-based Active Learning for Image Classification—0
Multi-loss ensemble deep learning for chest X-ray classification—0
Adaptive Region Pooling for Fine-Grained Representation Learning—0
Rethinking Client Reweighting for Selfish Federated Learning—0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning—0
Mistake-driven Image Classification with FastGAN and SpinalNet—0
Revisiting Linear Decision Boundaries for Few-Shot Learning with Transformer Hypernetworks—0
Noise-Contrastive Variational Information Bottleneck Networks—0
Invariance-Guided Feature Evolution for Few-Shot Learning—0
Interventional Black-Box Explanations—0
Informative Robust Causal Representation for Generalizable Deep Learning—0
Improvising the Learning of Neural Networks on Hyperspherical ManifoldCode0
Improving the Accuracy of Learning Example Weights for Imbalance Classification—0
Ontology-Driven Semantic Alignment of Artificial Neurons and Visual Concepts—0
When in Doubt, Summon the Titans: A Framework for Efficient Inference with Large Models—0
Use of small auxiliary networks and scarce data to improve the adversarial robustness of deep learning models—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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified