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 5301–5325 of 10420 papers

TitleStatusHype
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization—0
Improving Axial-Attention Network Classification via Cross-Channel Weight SharingCode0
Automated Seed Quality Testing System using GAN & Active LearningCode0
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)Code1
ResNet strikes back: An improved training procedure in timmCode1
TyXe: Pyro-based Bayesian neural nets for PytorchCode1
Perturbated Gradients Updating within Unit Space for Deep LearningCode0
Transferability Estimation for Semantic Segmentation Task—0
Towards Better Data Augmentation using Wasserstein Distance in Variational Auto-encoder—0
UNCERTAINTY QUANTIFICATION USING VARIATIONAL INFERENCE FOR BIOMEDICAL IMAGE SEGMENTATION—0
Revisiting Linear Decision Boundaries for Few-Shot Learning with Transformer Hypernetworks—0
LMSA: Low-relation Mutil-head Self-Attention Mechanism in Visual Transformer—0
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification—0
Are Vision Transformers Robust to Patch-wise Perturbations?—0
A precortical module for robust CNNs to light variations—0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning—0
An Investigation on Hardware-Aware Vision Transformer Scaling—0
A Dot Product Attention Free Transformer—0
Sample-specific and Context-aware Augmentation for Long Tail Image Classification—0
Self-supervised Models are Good Teaching Assistants for Vision Transformers—0
Representation Disentanglement in Generative Models with Contrastive Learning—0
Adaptive Region Pooling for Fine-Grained Representation Learning—0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING—0
Improving the Accuracy of Learning Example Weights for Imbalance Classification—0
Invariance-Guided Feature Evolution for Few-Shot 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