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 6376–6400 of 10420 papers

TitleStatusHype
Learning Invariant Representations across Domains and Tasks—0
Density estimation in representation space to predict model uncertainty—0
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework—0
Estimating the Generalization in Deep Neural Networks via Sparsity—0
Mixture of Experts in Image Classification: What's the Sweet Spot?—0
Learning Invariances in Neural Networks from Training Data—0
Mixture of Physical Priors Adapter for Parameter-Efficient Fine-Tuning—0
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit—0
Learning Interpretable Logic Rules from Deep Vision Models—0
Learning Interclass Relations for Image Classification—0
Learning in Confusion: Batch Active Learning with Noisy Oracle—0
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses—0
Mix-up Self-Supervised Learning for Contrast-agnostic Applications—0
Learning image quality assessment by reinforcing task amenable data selection—0
Learning Image Conditioned Label Space for Multilabel Classification—0
Learning Identity Mappings with Residual Gates—0
Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network—0
Morphological Network: How Far Can We Go with Morphological Neurons?—0
MLP-ASR: Sequence-length agnostic all-MLP architectures for speech recognition—0
MLP-based architecture with variable length input for automatic speech recognition—0
Analytic Expressions for Probabilistic Moments of PL-DNN With Gaussian Input—0
A Classification Leveraged Object Detector—0
Visually Consistent Hierarchical Image Classification—0
Learning Graph Structure for Multi-Label Image Classification via Clique Generation—0
Better (pseudo-)labels for semi-supervised instance segmentation—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