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 6426–6450 of 10420 papers

TitleStatusHype
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
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