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 4901–4925 of 10420 papers

TitleStatusHype
Data downlink prioritization using image classification on-board a 6U CubeSat—0
A Unified Scheme of ResNet and Softmax—0
Augmentation Policy Generation for Image Classification Using Large Language Models—0
Identification of Cervical Pathology using Adversarial Neural Networks—0
Identification of Seed Cells in Multispectral Images for GrowCut Segmentation—0
Time-Varying Propensity Score to Bridge the Gap between the Past and Present—0
Augmentation Inside the Network—0
Data Dropout: Optimizing Training Data for Convolutional Neural Networks—0
Accelerating Neural Architecture Exploration Across Modalities Using Genetic Algorithms—0
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning—0
Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise—0
Identifying Mislabeled Images in Supervised Learning Utilizing Autoencoder—0
Identifying regions of interest in whole slide images of renal cell carcinoma—0
Identifying Spurious Correlations and Correcting them with an Explanation-based Learning—0
How to distribute data across tasks for meta-learning?—0
A Kolmogorov Complexity Approach to Generalization in Deep Learning—0
Instant Adversarial Purification with Adversarial Consistency Distillation—0
If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components—0
iFlood: A Stable and Effective Regularizer—0
IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision—0
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification—0
Intelligent Cervical Spine Fracture Detection Using Deep Learning Methods—0
¶ILCRO: Making Importance Landscapes Flat Again—0
How to augment your ViTs? Consistency loss and StyleAug, a random style transfer augmentation—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