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 6101–6125 of 10420 papers

TitleStatusHype
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup—0
I-CNet: Leveraging Involution and Convolution for Image Classification—0
Pyramid Adversarial Training Improves ViT PerformanceCode0
Human Imperceptible Attacks and Applications to Improve Fairness—0
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data—0
Building extraction with vision transformer—0
OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images—0
Weakly-supervised Generative Adversarial Networks for medical image classification—0
On the Effectiveness of Neural Ensembles for Image Classification with Small Datasets—0
EffCNet: An Efficient CondenseNet for Image Classification on NXP BlueBox—0
Detecting Adversaries, yet Faltering to Noise? Leveraging Conditional Variational AutoEncoders for Adversary Detection in the Presence of Noisy Images—0
Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification—0
Reinforcement Explanation Learning—0
FedDropoutAvg: Generalizable federated learning for histopathology image classification—0
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network—0
ExPLoit: Extracting Private Labels in Split Learning—0
Global Interaction Modelling in Vision Transformer via Super Tokens—0
Application of deep learning to camera trap data for ecologists in planning / engineering -- Can captivity imagery train a model which generalises to the wild?—0
An Image Patch is a Wave: Phase-Aware Vision MLPCode0
Transferability Estimation using Bhattacharyya Class Separability—0
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation LearningCode0
Information Bottleneck-Based Hebbian Learning Rule Naturally Ties Working Memory and Synaptic Updates—0
Improved Fine-Tuning by Better Leveraging Pre-Training Data—0
Spatial-context-aware deep neural network for multi-class image classification—0
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning—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