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 8026–8050 of 10420 papers

TitleStatusHype
DeepAGREL: Biologically plausible deep learning via direct reinforcement—0
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST—0
Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches—0
Rethinking Hard-Parameter Sharing in Multi-Domain Learning—0
Rethinking Image Editing Detection in the Era of Generative AI Revolution—0
Automatic estimation of heading date of paddy rice using deep learning—0
A Local Detection Approach for Named Entity Recognition and Mention Detection—0
Between Progress and Potential Impact of AI: the Neglected Dimensions—0
Deep Adaptive Semantic Logic (DASL): Compiling Declarative Knowledge into Deep Neural Networks—0
Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach—0
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection—0
Frustratingly Easy Uncertainty Estimation for Distribution Shift—0
Improved OOD Generalization via Adversarial Training and Pre-training—0
Deep Active Learning in the Presence of Label Noise: A Survey—0
Automatic discovery of discriminative parts as a quadratic assignment problem—0
Improved Multi-Source Domain Adaptation by Preservation of Factors—0
Rethinking Persistent Homology for Visual Recognition—0
Rethinking Pseudo Labels for Semi-Supervised Object Detection—0
Rethinking Query, Key, and Value Embedding in Vision Transformer under Tiny Model Constraints—0
Deep Active Learning in the Open World—0
Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data—0
Improved Mix-up with KL-Entropy for Learning From Noisy Labels—0
Rethinking Skip Connection with Layer Normalization—0
Does Deep Active Learning Work in the Wild?—0
Improved Image Classification with Token Fusion—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