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 6526–6550 of 10420 papers

TitleStatusHype
MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900-2015)—0
MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization—0
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition—0
Convolutional Clustering for Unsupervised Learning—0
MUFold-SS: Protein Secondary Structure Prediction Using Deep Inception-Inside-Inception Networks—0
Generic Image Classification Approaches Excel on Face Recognition—0
A Survey on Visual Transformer—0
Generative NeuroEvolution for Deep Learning—0
Multi-Agent Mutual Learning at Sentence-Level and Token-Level for Neural Machine Translation—0
Convolutional autoencoder-based multimodal one-class classification—0
An Improved CNN-based Neural Network Model for Fruit Sugar Level Detection—0
Multi-branch CNN and grouping cascade attention for medical image classification—0
Multi-branch fusion network for hyperspectral image classification—0
Generative Negative Text Replay for Continual Vision-Language Pretraining—0
Convolutional Architecture Exploration for Action Recognition and Image Classification—0
Multiclass ASMA vs Targeted PGD Attack in Image Segmentation—0
Multiclass Burn Wound Image Classification Using Deep Convolutional Neural Networks—0
Aggregative Self-Supervised Feature Learning from a Limited Sample—0
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases—0
Multi-class Generative Adversarial Nets for Semi-supervised Image Classification—0
Dual-branch PolSAR Image Classification Based on GraphMAE and Local Feature Extraction—0
Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints—0
Generatively Augmented Neural Network Watchdog for Image Classification Networks—0
A survey on Semi-, Self- and Unsupervised Learning for Image Classification—0
Generative Data Mining with Longtail-Guided Diffusion—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