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 6401–6450 of 10420 papers

TitleStatusHype
Learning in Confusion: Batch Active Learning with Noisy Oracle—0
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses—0
MMF: Multi-Task Multi-Structure Fusion for Hierarchical Image Classification—0
MMFormer: Multimodal Transformer Using Multiscale Self-Attention for Remote Sensing Image Classification—0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning—0
MM-UNet: A Mixed MLP Architecture for Improved Ophthalmic Image Segmentation—0
MMViT: Multiscale Multiview Vision Transformers—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
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
Learning from Web Data: the Benefit of Unsupervised Object Localization—0
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate—0
Learning from Noisy Labels with Noise Modeling Network—0
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification—0
Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent—0
Modality-bridge Transfer Learning for Medical Image Classification—0
MoDE: CLIP Data Experts via Clustering—0
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling—0
Learning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling—0
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning—0
Model-Agnostic Feature Selection with Additional Mutual Information—0
Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search—0
Learning from Matured Dumb Teacher for Fine Generalization—0
Learning From Massive Noisy Labeled Data for Image Classification—0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning—0
Model-based feature selection for neural networks: A mixed-integer programming approach—0
A Simple Probabilistic Method for Deep Classification under Input-Dependent Label Noise—0
Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent—0
Learning from Large-scale Noisy Web Data with Ubiquitous Reweighting for Image Classification—0
Learning from Few Samples: A Survey—0
Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning—0
Learning from Exemplary Explanations—0
Learning from Crowds with Sparse and Imbalanced Annotations—0
Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use—0
Best Practices in Pool-based Active Learning for Image Classification—0
Demystifying What Code Summarization Models Learned—0
Demystifying Loss Functions for Classification—0
Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection—0
Best Practices for Convolutional Neural Networks Applied to Object Recognition in Images—0
Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification—0
Learning Fine-grained Features via a CNN Tree for Large-scale Classification—0
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration—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