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 28262850 of 10420 papers

TitleStatusHype
Biomed-DPT: Dual Modality Prompt Tuning for Biomedical Vision-Language ModelsCode0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
Anchor Loss: Modulating Loss Scale based on Prediction DifficultyCode0
Improving Pairwise Ranking for Multi-label Image ClassificationCode0
Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and RetrainingCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Improving Nonlinear Projection Heads using Pretrained Autoencoder EmbeddingsCode0
BioLCNet: Reward-modulated Locally Connected Spiking Neural NetworksCode0
Improving Axial-Attention Network Classification via Cross-Channel Weight SharingCode0
Improving model calibration with accuracy versus uncertainty optimizationCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Improving the repeatability of deep learning models with Monte Carlo dropoutCode0
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
Language-Driven Anchors for Zero-Shot Adversarial RobustnessCode0
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative LearningCode0
Data-Driven Neuron Allocation for Scale Aggregation NetworksCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
An Automated Ensemble Learning Framework Using Genetic Programming for Image ClassificationCode0
SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image ClassificationCode0
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck ModelsCode0
Improving Fairness in Image Classification via SketchingCode0
Bilinear CNNs for Fine-grained Visual RecognitionCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
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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