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 4026–4050 of 10420 papers

TitleStatusHype
Improved Image Classification with Token Fusion—0
Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image ClassificationCode0
Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries—0
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification—0
Conviformers: Convolutionally guided Vision TransformerCode0
DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning—0
Deep Autoencoder Model Construction Based on Pytorch—0
Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion ModelCode1
Teacher Guided Training: An Efficient Framework for Knowledge Transfer—0
Multi-Attribute Open Set RecognitionCode0
The SVD of Convolutional Weights: A CNN Interpretability Framework—0
Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation—0
Shuffle Instances-based Vision Transformer for Pancreatic Cancer ROSE Image ClassificationCode1
Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging—0
Entropy Induced Pruning Framework for Convolutional Neural Networks—0
Simulating Personal Food Consumption Patterns using a Modified Markov Chain—0
Dropout is NOT All You Need to Prevent Gradient LeakageCode0
The Weighting Game: Evaluating Quality of Explainability MethodsCode0
BEiT v2: Masked Image Modeling with Vector-Quantized Visual TokenizersCode0
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator—0
Contrastive Learning for OOD in Object detectionCode0
MixSKD: Self-Knowledge Distillation from Mixup for Image RecognitionCode1
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
Shifted Windows Transformers for Medical Image Quality Assessment—0
WeightMom: Learning Sparse Networks using Iterative Momentum-based pruning—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