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

Showing 27762800 of 10420 papers

TitleStatusHype
Improving robustness to corruptions with multiplicative weight perturbationsCode0
Boosting High Resolution Image Classification with Scaling-up TransformersCode0
Improving Pairwise Ranking for Multi-label Image ClassificationCode0
Improving Nonlinear Projection Heads using Pretrained Autoencoder EmbeddingsCode0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
Improving Transferability of Adversarial Examples with Input DiversityCode0
InterpNET: Neural Introspection for Interpretable Deep LearningCode0
Learning to Learn from Noisy Labeled DataCode0
Acne Severity Grading on Face Images via Extraction and Guidance of Prior KnowledgeCode0
SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image ClassificationCode0
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity MetricsCode0
Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You NeedCode0
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck ModelsCode0
Improving Generalization and Convergence by Enhancing Implicit RegularizationCode0
An Empirical Investigation of Randomized Defenses against Adversarial AttacksCode0
Boosting Deep Ensemble Performance with Hierarchical PruningCode0
Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K SamplingCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Improving Fairness in Image Classification via SketchingCode0
Boosting Adversarial Transferability across Model Genus by Deformation-Constrained WarpingCode0
1st Place Solution for ECCV 2022 OOD-CV Challenge Image Classification TrackCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified