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 5426–5450 of 10420 papers

TitleStatusHype
Efficient Converted Spiking Neural Network for 3D and 2D Classification—0
Efficient Computation of Quantized Neural Networks by −1, +1 Encoding Decomposition—0
CE-Dedup: Cost-Effective Convolutional Neural Nets Training based on Image Deduplication—0
Efficient CNNs via Passive Filter Pruning—0
A Non-monotonic Smooth Activation Function—0
CEC-CNN: A Consecutive Expansion-Contraction Convolutional Network for Very Small Resolution Medical Image Classification—0
A non-discriminatory approach to ethical deep learning—0
Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems—0
A Comparison of Few-Shot Learning Methods for Underwater Optical and Sonar Image Classification—0
Efficient Annotation for Medical Image Analysis: A One-Pass Selective Annotation Approach—0
Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management—0
Efficient and Reliable Overlay Networks for Decentralized Federated Learning—0
An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution—0
Efficient and Flexible Method for Reducing Moderate-size Deep Neural Networks with Condensation—0
CC-Loss: Channel Correlation Loss For Image Classification—0
Efficient Adaptive Ensembling for Image Classification—0
Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy—0
CCESAR: Coastline Classification-Extraction From SAR Images Using CNN-U-Net Combination—0
Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attacks—0
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network—0
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation—0
CBVLM: Training-free Explainable Concept-based Large Vision Language Models for Medical Image Classification—0
Effects of Image Degradations to CNN-based Image Classification—0
Effects of Auxiliary Knowledge on Continual Learning—0
Anomaly Detection in Image Datasets Using Convolutional Neural Networks, Center Loss, and Mahalanobis Distance—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
10RevCol-HTop 1 Accuracy90—Unverified