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

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Showing 3376–3400 of 10420 papers

TitleStatusHype
Biologically inspired deep residual networks for computer vision applications—0
Effective Evaluation of Deep Active Learning on Image Classification Tasks—0
Effective, Fast, and Memory-Efficient Compressed Multi-function Convolutional Neural Networks for More Accurate Medical Image Classification—0
Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method—0
Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation—0
Causally Focused Convolutional Networks Through Minimal Human Guidance—0
Effective Mutation Rate Adaptation through Group Elite Selection—0
Biologically Inspired Deep Learning Approaches for Fetal Ultrasound Image Classification—0
Anchor Cascade for Efficient Face Detection—0
Differentiable Deep Clustering with Cluster Size Constraints—0
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling—0
Effective Version Space Reduction for Convolutional Neural Networks—0
Differentiable Channel Sparsity Search via Weight Sharing within Filters—0
Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation—0
CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS—0
Anchor-based Nearest Class Mean Loss for Convolutional Neural Networks—0
Differentiable Architecture Compression—0
L_2BN: Enhancing Batch Normalization by Equalizing the L_2 Norms of Features—0
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network—0
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation—0
Bio-inspired learnable divisive normalization for ANNs—0
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network—0
A Closer Look at Personalization in Federated Image Classification—0
DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification—0
Bin-wise Temperature Scaling (BTS): Improvement in Confidence Calibration Performance through Simple Scaling Techniques—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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 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