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 6276–6300 of 10420 papers

TitleStatusHype
EXPLAINABLE AI-BASED DYNAMIC FILTER PRUNING OF CONVOLUTIONAL NEURAL NETWORKS—0
Clustered Task-Aware Meta-Learning by Learning from Learning PathsCode0
Revisiting Linear Decision Boundaries for Few-Shot Learning with Transformer Hypernetworks—0
Evaluating Language-biased image classification based on semantic compositionality—0
Ontology-Driven Semantic Alignment of Artificial Neurons and Visual Concepts—0
Chest X-Rays Image Classification from beta-Variational Autoencoders Latent Features—0
Noise-Contrastive Variational Information Bottleneck Networks—0
Causally Focused Convolutional Networks Through Minimal Human Guidance—0
Use of small auxiliary networks and scarce data to improve the adversarial robustness of deep learning models—0
An Investigation on Hardware-Aware Vision Transformer Scaling—0
UNCERTAINTY QUANTIFICATION USING VARIATIONAL INFERENCE FOR BIOMEDICAL IMAGE SEGMENTATION—0
A Dot Product Attention Free Transformer—0
On the Convergence of Nonconvex Continual Learning with Adaptive Learning Rate—0
Does deep learning model calibration improve performance in class-imbalanced medical image classification?—0
DM-CT: Consistency Training with Data and Model Perturbation—0
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding—0
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions—0
Measuring the Interpretability of Unsupervised Representations via Quantized Reversed Probing—0
Measuring the Effectiveness of Self-Supervised Learning using Calibrated Learning Curves—0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING—0
Sphere2Vec: Self-Supervised Location Representation Learning on Spherical Surfaces—0
Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods—0
Mako: Semi-supervised continual learning with minimal labeled data via data programming—0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning—0
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification—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